Entity representation identification using entity representation level information

ABSTRACT

Disclosed is a system for, and method of, searching for and identifying one or more entity representations using comprehensive search criteria built from known entity representations. The comprehensive search criteria are permitted to include inconsistent field values, that is, multiple different field values corresponding to the same field. The system and method may perform using search queries or batch files.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication No. 61/077,676 to Bayliss entitled “Database Systems AndMethods,” filed Jul. 2, 2008, the contents of which are herebyincorporated by reference.

The following patents and patent applications are related to the presentdisclosure and are hereby incorporated by reference in their entireties:

-   -   U.S. Pat. No. 7,293,024 entitled “Method for sorting and        distributing data among a plurality of nodes” to Bayliss et al.;    -   U.S. Pat. No. 7,240,059 entitled “System and method for        configuring a parallel-processing database system” to Bayliss et        al.;    -   U.S. Pat. No. 7,185,003 entitled “Query scheduling in a        parallel-processing database system” to Bayliss et al.;    -   U.S. Pat. No. 6,968,335 entitled “Method and system for parallel        processing of database queries” to Bayliss et al.;    -   U.S. patent application Ser. No. 10/357,447 entitled “Method and        system for processing data records” to Bayliss et al.;    -   U.S. patent application Ser. No. 10/357,481 entitled “Method and        system for linking and delinking data records” to Bayliss et        al.;    -   U.S. patent application Ser. No. 10/293,482 entitled        “Global-results processing matrix for processing queries” to        Bayliss et al.;    -   U.S. patent application Ser. No. 10/293,475 entitled “Failure        recovery in a parallel-processing database system” to Bayliss et        al.;    -   U.S. patent application Ser. No. 10/357,418 entitled “Method and        system for processing and linking data records” to Bayliss et        al.;    -   U.S. patent application Ser. No. 10/357,405 entitled “Method and        system for processing and linking data records” to Bayliss et        al.;    -   U.S. patent application Ser. No. 10/357,489 entitled “Method and        system for associating entities and data records” to Bayliss et        al.;    -   U.S. patent application Ser. No. 10/357,484 entitled “Method and        system for processing data records” to Bayliss et at.;    -   U.S. patent application Ser. No. 11/671,090 entitled “Query        scheduling in a parallel-processing database system” to Bayliss        et al.;    -   U.S. patent application Ser. No. 11/772,634 entitled “System and        method for configuring a parallel-processing database system” to        Bayliss et al.; and    -   U.S. patent application Ser. No. 11/812,323 entitled        “Multi-entity ontology weighting systems and methods” to        Bayliss.

The above applications are referred to herein as the “First GenerationPatents And Applications.” This disclosure may refer to variousparticular features (e.g., figures, tables, terms, etc.) in the FirstGeneration Patents And Applications. In the case of any ambiguity ofwhat is being referred to, the features as described in U.S. patentapplication Ser. No. 11/772,634 entitled “System and method forconfiguring a parallel-processing database system” to Bayliss et al.shall govern.

Also incorporated by reference in their entireties are U.S. ProvisionalPatent Application No. 61/047,570 entitled “Database systems andmethods” to Bayliss and U.S. Provisional Patent Application No.61/053,202 entitled “Database systems and methods” to Bayliss. Theseapplications are referred to herein as the “Second Generation PatentsAnd Applications.”

Also incorporated by reference in their entireties are U.S. patentapplication Ser. No. 10/866,456 entitled “System and method forreturning results of a query from one or more slave nodes to one or moremaster nodes of a database system” to Chapman et al., U.S. patentapplication Ser. No. 10/866,204 entitled “System and method forprocessing query requests in a database system” to Chapman et al., U.S.patent application Ser. No. 10/866,565 entitled “System and method forprocessing a request to perform an activity associated with aprecompiled query” to Chapman et al., and U.S. patent application Ser.No. 10/866,394 entitled “System and method for managing throughput inthe processing of query requests in a database system” to Chapman et al.These applications are referred to herein as the “'866 Applications.”This disclosure may refer to various particular features (e.g., figures,tables, terms, etc.) in the '866 Applications. In the case of anyambiguity of what is being referred to, the features as described inU.S. patent application Ser. No. 10/866,204 entitled “System and methodfor processing query requests in a database system” to Chapman et al.shall govern.

FIELD OF THE INVENTION

The invention relates to database systems and methods. Moreparticularly, the invention relates to a technique for identifying oneor more entity representations using comprehensive search criteria builtfrom one or more known entity representations.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, both as to its structure and operation together with theadditional objects and advantages thereof are best understood throughthe following description of exemplary embodiments of the presentinvention when read in conjunction with the accompanying drawings.

FIG. 1 is a flowchart depicting an embodiment of an invention of SectionI.

FIG. 2 is a flowchart depicting an embodiment of an invention of SectionII.

FIG. 3 a is a flowchart depicting an embodiment of an invention ofSection III.

FIG. 3 b is an exemplary network diagram depicting the partitioning ofthe batch file and the universal database into the one or more nodesaccording to an embodiment of an invention of Section III.

FIG. 4 is a flowchart depicting an embodiment of an invention of SectionIV.

FIG. 5 is a flowchart depicting an embodiment of an invention of SectionV.

FIG. 6 is a flowchart depicting an embodiment of an invention of SectionVI.

FIGS. 7A, B and C are flowcharts depicting embodiments of an inventionof Section VII.

FIG. 8 is a flowchart depicting an embodiment of an invention of SectionVIII.

DETAILED DESCRIPTION

The following detailed description presents several inventive concepts,which are inter-related. The following Table of Contents summarizes thepresent disclosure.

Table of Contents Section Statistical Measure And Calibration Of SearchCriteria Where One Or Both Of The I Search Criteria And Database IsIncomplete Entity Representation Identification Based On A Search QueryUsing Field Match II Templates Batch Entity RepresentationIdentification Using Field Match Templates III Method Of PartitioningMatch Templates IV Statistical Measure And Calibration Of InternallyInconsistent Search Criteria V Where One Or Both Of The Search CriteriaAnd Database Is Incomplete Statistical Measure And Calibration OfReflexive, Symmetric And Transitive VI Fuzzy Search Criteria Where OneOr Both Of The Search Criteria And Database Is Incomplete EntityRepresentation Identification Using Entity Representation LevelInformation VII Technique For Recycling Match Weight Calculations VIIIConclusion IX

Certain terms used herein are discussed presently. The term “entityrepresentation” encompasses at least one record, and, more typically, acollection of linked records that refer to the same individual. Thisterm is meant to embrace the computer implemented entities of the FirstGeneration Patents And Applications. The term “field” encompasses anyportion of a record into which a field value may be entered. The term“field value” encompasses means and manners used to representinformation, not limited to numerical values. A “field value” mayinclude other types of data values comprising one or more charactertypes or combination of character types. This term is meant to embracethe “data field values” of the First Generation Patents AndApplications. The term “individual” encompasses a natural person, acompany, a body of work, and any institution. The term “probability”encompasses any quantitative measure of likelihood or possibility, notlimited to numerical quantities between zero and one. The term “record”encompasses any data structure having at least one field. This term ismeant to embrace the “entity references” of the First Generation PatentsAnd Applications. The discussion in this paragraph is meant to provideinstances of what is embraced by certain terms by way of non-limitingexample and should not be construed as restricting the meaning of suchterms.

The present document includes disclosures of several inventions, whichare presented in the following Sections I-IX. Embodiments of theseinventions may interact and work together with each other and with thesystems and methods presented in the First Generation Patents AndApplications, the Second Generation Patents And Applications and the'866 Applications. For example, parameters generated by an embodiment ofan invention presented in one section may be used by an embodimentpresented in another section, in the First Generation Patents AndApplications, in the Second Generation Patents And Applications, or inthe '866 Applications. Exemplary details of such interaction arepresented herein.

I. Statistical Measure and Calibration of Search Criteria Where One orBoth of the Search Criteria and Database is Incomplete

Techniques according to this section may be used to identify anindividual in response to a query (e.g., by identifying a record orentity representation associated with such individual). Some embodimentsmay be implemented with respect to a database that contains a pluralityof records, entity representations, or a combination thereof.Embodiments of the techniques of this section may receive a query thatspecifies or constrains the field values for one or more fields. Suchembodiments may proceed to identify the record or entity representationthat most likely corresponds to individual identified by the query.

The contents of certain databases may be restricted by legal or otherconstraints. Examples of such databases include databases of medicalrecords, financial information, educational information, or otherrestricted data. The contents of the such databases may be protected bylaws including, but not limited to: the Health Insurance Portability andAccountability Act (“HIPAA”), the Gramm-Leach-Bliley Act (“GPA”), or theFamily Educational Rights and Privacy Act (“FERPA”). Such databases maythus exclude unfettered access by a practitioner of a techniqueaccording to this section. Databases that are owned, operated,accessible to or used by a third party are referred to herein as“foreign.” Examples of foreign databases include, but are not limitedto, databases in which access is restricted.

A practitioner of the techniques of this section may own, operate orhave access to a comprehensive database. Such a database may be intendedto be complete, relative to a foreign database, in the sense that it maybe intended to include an entity representation corresponding to all orsubstantially all individuals for which an entity representation existsin the foreign database. Such a database may be referred to herein as“universal.” Note, however, that a universal database may not actuallybe complete in the sense that it may omit records or entityrepresentations that correspond to individuals that are represented inone or more foreign databases. A universal database may have undergonean iterative linking process and associated processes as disclosed inone or both of the First Generation Patents And Applications and theSecond Generation Patents And Applications in order to generate accurateentity representations from raw data. In particular, a universaldatabase may include definitive identifiers (“DID”) as discussed in theFirst Generation Patents And Patent Applications. Other techniques forlinking records and forming entity representations may be employed inthe alternative; however, the present discussion will be with respect toDIDs.

In some embodiments, practitioners of the techniques disclosed hereinmay operate on foreign database data without violating restrictions onsuch data. This may be accomplished, by way of nonlimiting example, asfollows. The practitioner may set up a hardened computing facility,which may be made physically inaccessible to unauthorized persons by wayof one or more of: physical locks, biometric identificationrequirements, human guards and electronic intrusion detection. Moreover,the computing facility may include computers that are not linked to anycomputer outside the facility and not on any network that extends beyondthe secure computing facility itself. The facility may enforce a rulethat only a customer that supplies data to the faculty may acquire datafrom the facility. Thus, a customer owning or having rightful access toa foreign database may supply data from the foreign database to thecomputing facility. While the practitioner may operate on the data inthe facility, the practitioner may be prevented from removing any datafrom the facility. Once operations on the data are complete, thecustomer may retrieve data from the facility. Upon the customerretrieving such data, the computers in the facility may be secured byflushing volatile memory and securely deleting data from the persistentmemory (e.g., by writing random data to the persistent memory multipletimes). Thus, a practitioner of the present technique may operate on aforeign database without violating laws or regulations that governaccess to such data.

Embodiments according to this section may be used to identify entityrepresentations in a foreign database. This may be accomplished inseveral ways. For example, a query that is intended to identify aparticular individual may be issued to a universal database. Thetechniques of this section may be utilized to identify an entityrepresentation in the universal database that matches the query. Oncethe individual is identified using the universal database, more completeinformation about the individual (as compared to the informationspecified by the query) may be retrieved from the universal database andsupplied to an operator of the foreign database. The operator may thenidentify records or entity representations in the foreign database thatcorrespond to the more complete information acquired from the universaldatabase. In particular, the information retrieved from the universaldatabase in response to a query may include one or more keys that indexone or more records in the foreign database.

In general, the foreign database may be amended to include entityrepresentation identifiers (by way of non-limiting example, DIDs) of theuniversal database. This technique allows for immediate retrieval of oneor more records from the foreign database based on one or more DIDsidentified by a query to the universal database. This process mayproceed by first establishing a secure computing facility for data fromthe foreign database as discussed above. The facility may then beprovisioned with a copy of the foreign database (or equivalent data).Now, each record in the foreign database corresponds to some individual.Furthermore, each such individual will likely have an associated entityrepresentation in the universal database. As a result of applying thetechniques of this section (or those of Sections II or III), each recordin the foreign database in the secure facility may have appended to it aDID for the associated entity representation in the universal database.This may be accomplished, for example, using queries, or in one or morebatch processes. For a query approach, each record from the copiedforeign database may be used to form a query by specifying the fieldvalues present in such record. Each query may be submitted to theuniversal database, which may or may not be external to the facility.The universal database may process each query using a technique of thissection in order to identify a corresponding entity representation. TheDID of that entity representation may then be transferred back into thesecure facility (if it is not already there, e.g., if the securefacility contains a copy of the universal database) and appended to therecord that generated the query. This process may be repeated for eachrecord in the copied foreign database until each record therein has aDID for the corresponding entity representation in the universaldatabase appended thereto. Note further that this process may be done inbatch form, on a parallel computer, or a combination of both.

Among other benefits of this approach is that the operator of theforeign database may discover multiple records for the same individual.For example, a bank may have multiple accounts held by the sameindividual, or a retailer may have multiple accounts for the sameindividual. By associating each record in the foreign database with aDID, the operator of the foreign database can determine that twodifferent records have the same DID and are therefore associated withthe same individual. A bank armed with such knowledge may be better ableto serve the customer once it realizes that the customer holds multipleaccounts, and the retailer may omit duplicative mailings, for example.

A batch processing approach may proceed as follows. One or more groupsof records in the foreign database may be processed together in a batch.Each group may be a small as a single record, as big as the entireforeign database, or any size in between. Each record group may beprocessed according to one or more of the techniques disclosed herein.More particularly, each record group may be applied against theuniversal database in order to identify a DID for each record therein.Such DIDs are, as in the query approach, associated with the entityrepresentations in the universal database. Once each record in a groupis associated with a DID, the foreign database may be amended to includesuch DIDs in association with the records. This process may proceed toprocess records from the foreign database until each such record has anappended DID or a determination is made for such record that a D fromthe universal database is unavailable. Note that the batch processingapproach is suited for foreign databases that do not have accessrestrictions, such that batches of records may be transferred to thesecure computing facility or another facility. For foreign databasesthat have access restrictions, the computing facility may be modified byincluding a copy of the universal database In such an arrangement, thesecure computing facility may accomplish the batch processing withoutany record from the foreign database leaving the secure computingfacility (until the customer retrieves the processed data).

Whether a query-based approach is used or a batch processing approach isused, a result may be that each record (or substantially all records) inthe foreign database has an appended DID that corresponds to an entityrepresentation in the universal database. Thus, queries aboutindividuals reflected in the foreign database may be processed bysubmitting such query to the universal database, determining an entityrepresentation in the universal database identified by the query,retrieving the associated DID, and then locating a record in the foreigndatabase by using that DID. Thus, queries regarding the foreign databasemay be processed without needing to access the foreign database untilthe moment when the record or entity representation is retrieved.

FIG. 1 is a flowchart depicting an embodiment of an invention of SectionI. An exemplary embodiment of a technique for processing a query to auniversal database (or other database) in order to identify one or morerecords is discussed presently. The technique may generally includereceiving a query and then outputting a DID (or other entityrepresentation identifier) for one or more records that correspond tothe query. As discussed in detail above, one application of the presenttechnique is in submitting a query to a universal database in order toidentify an entity representation in a foreign database by way of theDID produced by the present technique. However, embodiments of thepresent technique are not limited to such instances and may be used toprocess queries generally. That is, the present technique may be appliedto a universal database for the purpose of identifying a record in aforeign database, or may be applied to any general purpose database inorder to identify a record therein. Thus, discussion of the presenttechnique will be made in reference to a “database,” identified at block105, which may be universal or otherwise. The exemplary database underdiscussion may have undergone an iterative linking process and otherprocesses as disclosed in the First Generation Patents And Applicationsor the Second Generation Patents And Applications such that the databasecontains a plurality of entity references, each (or substantially each)of which consists of a plurality of records linked according to sharedDIDs. Alternately, the exemplary database under discussion may consistof unlinked records; in such instance, record identifications may beused as DIDs.

For purposes of discussion, a portion of a database with recordscontaining a first name field (“FN”), a last name field (“LN”), a statefield (“ST”), a zip code field (“ZIP”), a social security number field(“SSN”) and a definitive identifier field (“DID”) is presented below.

TABLE I.1 DID FN LN ST ZIP SSN 1 John Smith Florida 999-99-9999 1 JohnSmith 33446 2 Jane Smith Virginia 888-88-8888 2 J. Smith 888-88-8888 3Jane Doe Florida 777-77-7777 4 Bill Doe Michigan 5 John Doe Nevada 89146

The exemplary embodiment may proceed as follows. At block 110, a tablemay be generated for some or all (non-DID) fields in any record in thedatabase; such tables are referred to as “field tables.” Each fieldtable may include a column for a field value, a column for weightsassociated with the field values, and a column for an associateddefinitive identifier. The weights may be, by way of non-limitingexample, field weights or field value weights as disclosed in the SecondGeneration Patents And Applications. The field tables may have repeatentries removed. In some embodiments, the field tables omit the fieldvalue column. Exemplary tables that correspond to Table I.1 above appearbelow.

TABLE I.2 FN Field Table Field Value Weight DID John 4 1 Jane 5 2 J. 2 2Bill 6 4 John 4 5

TABLE I.3 LN Field Table Field Value Weight DID Smith 7 1 Smith 7 2 Doe6 3 Doe 6 4 Doe 6 5

TABLE I.4 ST Field Table Field Value Weight DID Florida 8 1 Virginia 7 2Michigan 6 4 Nevada 11 5

TABLE I.5 ZIP Field Table Field Value Weight DID 33446 22 1 89146 25 5

TABLE I.6 SSN Field Table Field Value Weight DID 999-99-9999 22 1888-88-8888 22 2 777-77-7777 22 3

At block 115, the exemplary embodiment proceeds by receiving a querythat specifies or constrains at least one field value. The receivedsearch field value data may be in the form of, by way of nonlimitingexamples, a query or a set of specific field values. Continuing thespecific example under discussion, an exemplary query may be of theform: {FN=John & LN=Smith & ZIP=33446 & ST=Florida}. Exemplary andsuitable query forming and processing techniques and hardware aredisclosed in the First Generation Patents And Applications. At block120, the embodiment proceeds to perform a fetch operation for eachspecified search criterion. In this instance, there are four fetches:one performed on the FN field table for FN=John, one performed on the LNfield table for LN=Smith, one performed on the zip code field table forZIP=33446, and one performed on the state field table for ST=Florida.The first fetch returns the first and fifth rows of the FN field table;the second fetch returns the first and second rows of the LN fieldtable, the third fetch returns the first row of the ZIP field table, andthe fourth fetch returns the first row of the ST field table. At block125, these results are then joined according to DID, and at block 130the weights summed for each DID. A table resulting from the exampleunder discussion is produced below.

TABLE I.7 FN LN ST ZIP Summed DID FN Weight LN Weight ST Weight ZIPWeight Weights 1 John 4 Smith 7 Florida 8 33446 22 41 2 Smith 7 7 5 John4 4

Note that in some embodiments, the table may omit the field values.Next, at block 135, the results are sorted by summed weights. In thepresent example, the results that appear in Table I.7 are already sortedby summed weights, so no manipulation is required in this example. Dueto the way that the table was created, the first record is the mostlikely record to correspond to the query.

Next, at block 140, a confidence level is assessed regarding whether thefirst record in the table is indeed a record specified by the query.That is, a determination is made as to whether it is sufficientlyprobable that the first record is responsive to the query. There areseveral techniques that may be used to make such an assessment.

A first technique for gauging whether the first record is correctfollows. The score for the first record is compared to the score of thesecond record. If the difference between the first record's score andthe second record's score is at least −log(1−C), where C is a confidencelevel expressed as a probability, then the probability that the firstrecord is indeed correct is at least C. This relation may be expressedas, by way of non-limiting example:

w ₁ −w ₂≧−log(1−C).   Equation 1

(Formally, the expression above is a relation; however, for convenienceit will be referred to as “Equation 1.”) In Equation 1, w₁ representsthe score for the first record, w₂ represents the score for the secondrecord, and C represents a selected confidence level. In Equation 1, andthroughout this disclosure, by way of non-limiting example, the logfunction has as its base two (2). Nevertheless, other bases may be usedin embodiments of the present inventions, such as, by way ofnon-limiting example, 2, 3⅓ or 10. If the relation expressed in Equation1 holds, then the first record is correct with a confidence level of C.

Applying Equation 1 to the example under discussion, the score of thefirst record is 41, and the score of the second record is 7. Thus, thedifference between the scores for the first and second records isw₁−w₂=41−7=34. Suppose that circumstances require that the confidencelevel be at least 99%. Then, by applying Equation 1, a determination ismade as to whether 34 is at least as great as −log(1−0.99). Because−log(1−0.99)=6.64, which is less than 34, the confidence level is met.

A second technique for gauging whether the first record is correctfollows. For the second technique, the scores for all of the records inthe results table (e.g., Table I.7) are used. For the second technique,the confidence level may be calculated as one minus the sum of thereciprocals of two raised to the power of the differences between theweight for the first record and the weights for the other records.Formally, this may be expressed as, by way of non-limiting example:

$\begin{matrix}{C = {1 - {\sum\limits_{i}{\frac{1}{2^{w_{1} - w_{i}}}.}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In Equation 2, C represents the confidence level that the first recordis correct, the term w₁ represents the score for the first record, andeach w_(i) represents the weight of the i-th record. The sum in Equation2 is over all weights in the results table. Applying Equation 2 to theexample under discussion yields, by way of non-limiting example:

$\begin{matrix}{C = {{1 - \frac{1}{2^{w_{1} - w_{2}}} + \frac{1}{2^{w_{1} - w_{3}}}}\mspace{20mu} = {{1 - \frac{1}{2^{41 - 7}} + \frac{1}{2^{41 - 4}}} = {0.9999.}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

Thus, according to Equation 3, the confidence level that the firstrecord is the correct record (i.e., that it correctly matches the query)is at least 99.99%. If the confidence level computed according toEquation 2 meets or exceeds a specified confidence level, then the firstrecord may be considered correct. Note that this second technique may beimplemented by first specifying C and then determining whether the firstrecord meets the selected confidence level.

A third technique for gauging whether the first record is correct issimilar to the second technique. For the third technique, instead ofcomputing the entire sum of Equation 2, the sum is computed only for thefirst few scores after the first score. In non-limiting exemplaryembodiments, the sum may be computed over the second score alone, overthe second and third scores, over the second through fifth scores, orover the second through tenth scores. Other sums are possible. Otherthan the number of scores taken into account, the third technique mayproceed according to the second technique.

The first three techniques for gauging whether the first record in theresults table is correct are particularly suitable when the universaldatabase contains records for every individual reflected in the foreigndatabase. However, that situation may not always be the case.Accordingly, a fourth technique is provided, where the fourth techniquethat provides accurate results even when the universal database is notcomplete. That is, the fourth technique is particularly suited forsituations where the query is meant to identify an individualrepresented in a foreign database, where the query is made to auniversal database (this situation is discussed in detail above in thissection), but where the universal database is not complete relative tothe foreign database. The fourth technique thus provides accurateresults even when the foreign database contains records for individualsthat are not represented by any records in the universal database. Notehowever, that any technique discussed herein may be applied in anysituation, not limited to those described herein as being particularlysuited to it

A detailed description of the fourth technique for gauging whether thefirst record is correct follows. The fourth technique accounts for adifference between the universal database and the foreign database inthe following way. If the universal database reflects U individuals,where U is a number, and the foreign database reflects F individuals,where F is a number, then there are F−U individuals accounted for in theforeign database but unaccounted for in the universal database. Thisunaccounted for population may be essentially treated as a monolithicunknown set of individuals. Thus, the fourth technique allows for aconclusion that the first record is correct with a confidence level ofC, where C is a probability, when the following equation obtains:

w ₁≧log(F−U)−log(1−C).   Equation 4

In Equation 4, w₁ represents the score of the first record and C, U andF are as described above. Note that in some embodiments, the quantityF−U is approximated. This may be accomplished in several ways. By way ofnon-limiting example, if a foreign database holds records for peoplethat are between 16 and 25 years old (inclusive), and the universaldatabase holds records for people that are between 18 and 25 years old(inclusive), then F−U may be approximated as 20% multiplied by thenumber of entity representations in the foreign database. (This isbecause the foreign database holds records for people of ten differentages, yet the universal database holds records of eight different ages,a difference of two years, or 2/10=20%.) In some embodiments, inaddition to Equation 4 being satisfied, Equation 1 is also checked todetermine whether it is satisfied. In other embodiments, the techniqueof Equation 4 may be combined with any of the first three techniques.That is, in such embodiments, the first record satisfies Equation 4 andpasses the tests set forth according to any of the first threetechniques. In some such embodiments, the same confidence level may be aparameter in the equations of two or more techniques.

Note that the techniques described in reference to Equations 1-4 are notlimited to application to tables formed according to the techniquesdiscussed in reference to I.1-I.7 above. For example, the techniquesdescribed in reference to Equations 1-4 may be used to determine whetherresults produced according to any of the techniques presented herein(e.g., in Sections II or III) are sufficiently reliable.

If the assessed confidence level passes the selected test according toany of Equations 1-4 above, at block 145 the technique outputsinformation reflecting the identified entity representation. In someembodiments, a DID of the highest ranked entity reference is output; inother embodiments, other information, such as a social security number,may be output. Note that the output may be via a monitor in ahuman-readable form, to another computer in computer-readable form, orin any other form that sufficiently identifies the result.

According to an exemplary embodiment, a method for identifying an entityrepresentation associated with a universal database that corresponds tosearch criteria associated with a foreign database is disclosed. Themethod includes, for each search criterion of the search criteria,separately fetching a set of data, each set of data including portionsof at least one record from the universal database, each portionincluding a weight and an individual identifier. The method alsoincludes merging the sets of data according to individual identifiers,resulting in merged sets of data. The method further includesdetermining a merged set of data with a greatest cumulative weight. Themethod further includes calculating a confidence level that a recordfrom the universal database corresponding to the merged set of data withthe greatest cumulative weight matches the search criteria.

II. Entity Representation Identification Based on a Search Query UsingField Match Templates

Techniques according to this section may be used to identify anindividual in response to a query (e.g., by identifying a record orentity representation associated with such individual). Embodiments ofthe techniques of this section may receive a query that specifies orconstrains the field values for one or more fields. Such embodiments mayproceed to identify the record or entity representation that most likelycorresponds to the individual identified by the query.

In general, the techniques described in this section may provide apractical application of the techniques of processing search criteria toa universal database (or other database) in order to identify one ormore records as described in Section I. Such techniques may generallyinclude receiving a query and then outputting a DID (or other entityrepresentation identifier) for one or more records that correspond tothe query. For ease of discussion and without limitation, the followingwill be in reference to DIDs, with the understanding that otheridentification or linking schemas may be used. Embodiments of thetechniques of this section may output a DID that most likely correspondsto a query when data associated with a universal database is incompleteor erroneous, data associated with the query is incomplete or erroneous,or a combination of both. Such embodiments may proceed to identify a DIDthat most likely corresponds to the query using a finite number of fieldmatch templates.

FIG. 2 is a flowchart depicting an exemplary embodiment according tothis section. At block 205, a plurality of field match templates areconstructed.

In various embodiments, the techniques of this section and othersections may begin by constructing one or more field match templatesthat may be used to partition a given search criteria (e.g., a query)according to (1) fields that must be populated and match, referred to as“fixed” fields, (2) fields that must match if populated, referred to as“optional” fields, and (3) fields that need not match, but that arecounted toward a match score if populated and a match occurs, referredto as “extra credit” fields.

That is, for a record to be considered to match a search criteria, allfields in the record that a field match template designates as fixedmust be populated with field values that match the corresponding searchcriteria fixed field values. Otherwise the entire record is considerednot to match the search criteria.

For a record to be considered to match a search criteria, fields in arecord that a field match template designates as optional may be blank(i.e., null) or, if populated with field values, such field values mustmatch the corresponding optional field values of the search criteria.Otherwise the entire record is considered not to match the searchcriteria.

A record may be considered to match a search criteria even if there isnot a match in a field designated extra-credit. For example, fields in arecord that a field match template designates as extra-credit may beblank (i.e., null) or populated with field values that do not match thecorresponding extra-credit field values of the search criteria, therecord would still be considered a match to the search criteria. If theextra-credit fields of the record are populated with field values thatmatch the corresponding extra-credit field values of the searchcriteria, the field values of the extra-credit fields of the record arecounted toward a match score. Otherwise, the field values of theextra-credit fields of the record are not counted toward a match score.In some embodiments, the associated field value weight may be subtractedfrom a match score.

In some embodiments, a field match template may be constructed based onone or more of the most popular queries as determined by accessing oneor more query logs associated with a database.

In various embodiments, a field match template may include one or morefixed fields, zero or more optional fields, a DID field, and zero ormore extra credit fields. In such embodiments, the sequence of a fieldmatch template may be ordered such that one or more fixed fields arefirst, followed by one or more optional fields, and one or more extracredit fields are last. The DID field may be placed after one or morefixed fields, after one or more optional fields, or before one or moreextra credit fields.

In symbols, a field match template may be represented as, by way ofnon-limiting example: (FN, LN, ST, DID, CITY). In this example, thesymbol “FN” may correspond to a first name field, the symbol “LN” maycorrespond to a last name field, the symbol “ST” may correspond to astate field, and the symbol “CITY” may correspond to a city field. Thesymbol “DID” may correspond to a definitive identifier described in theFirst Generation Patents and Applications. In this example, the firstname field and last name field may be fixed fields, the state field maybe an optional field, and the city field may be an extra credit field.Another field match template may be represented as, by way ofnon-limiting example: (FN, LN, DID, ST, CITY). In this example, thefirst name field and last name field may be fixed fields, while thestate field and city field may be extra credit fields. Yet another fieldmatch template may be represented as, by way of non-limiting example:(FN, LN, ST, CITY, DID). In this example, the first name field and lastname field may be fixed fields, while the state field and city field maybe optional fields. Match templates may include internal indicia thatdesignate where the partitions between fixed, optional and extra creditfields occur. The exact form in which match templates are electronicallystored may vary.

In various embodiments, records stored in the database may be storedaccording to the methods described in the '866 Applications.Accordingly, one or more records of the database may be stored indistributed tables sorted by one or more fields associated with a fieldmatch template. In some embodiments, the columns (e.g., fields) of thedistributed tables may be ordered in a particular sequence. In suchembodiments, the particular sequence of the columns of a distributedtable may be determined based on the sequence of fields of a field matchtemplate associated with the distributed table. For example, a fieldmatch template represented as (FN, LN, ST, DD, CITY), where the firstname field and the last name field of the field match template are fixedfields, the state field of the field match template is an optionalfield, and the city field of the field match template is extra credit,may be associated with one or more distributed tables with recordsstored sorted by a first name field, a last name field, a state field,and a DID field.

It is noted that, in some embodiments, the database may be implementedin a SQL relational database management system environment. In suchembodiments, the fixed fields may be the columns of an indexed table.

At block 210, a plurality of distributed tables that are associated withone or more field match templates are provided. In various embodiments,one or more distributed tables associated with a field match templatemay be stored sorted by the fixed fields, the optional fields, the DIDfield of the field match template, or a combination thereof. Thus, theone or more distributed tables associated with the field match templatedescribed above may be sorted by the first name field, then sorted bythe last name field, then sorted by the state field, and then sorted bythe DID field. In some embodiments, extra credit fields may not effectthe way in which records associated with a distributed table are stored.By way of non-limiting example, a portion of a database associated witha field match template represented as: (FN, LN, ST, DID, CITY) may besorted and stored as depicted below.

TABLE II.1 FN LN ST DID CITY Brian Adams Alabama 21 Birmingham BrianAdams Florida 82 Tampa Brian Adams New York 5 Syracuse Brian AndersonCalifornia 48 San Diego Brian Anderson California 96 Los Angeles BrianAnderson California 132 San Diego

As depicted in Table II.1, the technique may store one or more recordsof the database in a distributed table sorted by one or more fixedfields, one or more optional fields, and a DID field associated with afield match template. Thus, one or more search results fetched from thedistributed table may be returned sorted. Accordingly, the DID field maybe strategically placed (e.g., after the one or more fixed fields or oneor more optional fields and before one or more extra credit fields) in afield match template to enable one or more search results of givensearch criteria (e.g., a query) to be returned in a sorted state withoutthe need to separately sort the returned results.

In various embodiments, a distributed database associated with a fieldmatch template may be distributed over and stored on one or more nodesas described in the FIGS. 1 and 2 of the '866 Applications. Accordingly,this technique may support parallel processing of given search criteria.

For purposes of discussion, a portion of a database with recordscontaining a first name field (“FN”), a middle name field (“MN”), a lastname field (“LN”), an age field (“AGE”), a city field (“CITY”), a statefield (“ST”), and a definitive identifier field (“DID”) is reproducedbelow.

TABLE II.2 DID FN MN LN AGE CITY ST 1 Jon Ron Doe 39 Miami 1 Jon DoeFlorida 2 John Ronald Doe Florida 3 Jack Ron Doe 72 3 Jack Ronald DoeTampa Florida 3 Jack Doe 72 4 John Ron Doe Tampa 4 John Doe 32 Florida 4John Ronald Doe Tampa 5 Jason Rick Doe 31 Orlando 5 Jason Doe 31 Florida6 John Doe 32 6 John Ronald Doe Florida 6 John Doe 32 Tampa Florida 6John Ron Doe Florida 7 John Ronald Doe 21 Orlando Florida 7 John Doe 21Florida

According to this exemplary embodiment, three field match templates maybe defined as follows: Field Match Template A: (FN, MN, LN, DID) whereall the non-DID fields of Field Match Template A are fixed fields, FieldMatch Template B: (FN, LN, MN, AGE, DD) where the first name field andthe last name field of Field Match Template B are fixed fields and themiddle name field and the age field of Field Match Template B areoptional fields, and Field Match Template C: (FN, LN, ST, DID, CITY)where the first name field, last name field, and state field of FieldMatch Template C are fixed fields and the city field of Field MatchTemplate C is an extra credit field.

As discussed above and according to this exemplary embodiment, theportion of the database reproduced in Table II.2 may be stored in one ormore distributed tables associated with Field Match Template A, one ormore distributed tables associated with Field Match Template B, and oneor more distributed tables associated with Field Match Template C. Toillustrate, Table II.3A, a distributed table associated with Field MatchTemplate A, Table II3B, a distributed table associated with Field MatchTemplate B, and Table II.3C, a distributed table associated with FieldMatch Template C are produced below, respectively.

TABLE II.3A FN MN LN DID Jack Ron Doe 3 Jack Ronald Doe 3 Jack Doe 3Jason Rick Doe 5 Jason Doe 5 John Ron Doe 4 John Ron Doe 6 John RonaldDoe 2 John Ronald Doe 4 John Ronald Doe 6 John Ronald Doe 7 John Doe 4John Doe 6 John Doe 6 John Doe 7 Jon Ron Doe 1 Jon Doe 1

TABLE II.3B FN LN MN AGE DID Jack Doe Ron 72 3 Jack Doe Ronald 3 JackDoe 72 3 Jason Doe Rick 31 5 Jason Doe 31 5 John Doe Ron 4 John Doe Ron6 John Doe Ronald 21 7 John Doe Ronald 2 John Doe Ronald 4 John DoeRonald 6 John Doe 21 7 John Doe 32 4 John Doe 32 6 John Doe 32 6 Jon DoeRon 39 1 Jon Doe 1

TABLE II.3C FN LN ST DID CITY Jack Doe Florida 3 Tampa Jack Doe 3 JackDoe 3 Jason Doe Florida 5 Jason Doe 5 Orlando John Doe Florida 2 JohnDoe Florida 4 John Doe Florida 6 John Doe Florida 6 Tampa John DoeFlorida 6 John Doe Florida 7 Orlando John Doe Florida 7 John Doe 4 TampaJohn Doe 4 Tampa John Doe 6 Jon Doe Florida 1 Jon Doe 1 Miami

In various embodiments, a field value weight field associated with eachnon-DID field value in a distributed table may be stored in thedistributed table. Accordingly, a field value weight field associatedwith each non-DID field in Table II.3A, Table II.3B, and Table II.3C maybe stored in Table II.3A, Table II.3B, and Table II.3C. To illustrate,Table II.4A with a field value weight field for each non-DID field inTable II.3A, Table II.4B with a field value weight field for eachnon-DID field in Table II.3B, and Table II.4C with a field value weightfield for each non-DID field in Table II.3C are produced below.

TABLE II.4A FN_Value MN_Value LN_Value FN MN LN DID Weight Weight WeightJack Ron Doe 3 8 3 6 Jack Ronald Doe 3 8 5 6 Jack Doe 3 8 6 Jason RickDoe 5 5 6 6 Jason Doe 5 5 6 John Ron Doe 4 4 3 6 John Ron Doe 6 4 3 6John Ronald Doe 2 4 5 6 John Ronald Doe 4 4 5 6 John Ronald Doe 6 4 5 6John Ronald Doe 7 4 5 6 John Doe 4 4 6 John Doe 6 4 6 John Doe 6 4 6John Doe 7 4 6 Jon Ron Doe 1 8 3 6 Jon Doe 1 8 6

TABLE II.4B FN_Value LN_Value MN_Value AGE_Value FN LN MN AGE DID WeightWeight Weight Weight Jack Doe Ron 72 3 8 6 3 15 Jack Doe Ronald 3 8 6 5Jack Doe 72 3 8 6 15 Jason Doe Rick 31 5 5 6 6 10 Jason Doe 31 5 5 6 10John Doe Ron 4 4 6 3 John Doe Ron 6 4 6 3 John Doe Ronald 21 7 4 6 5 14John Doe Ronald 2 4 6 5 John Doe Ronald 4 4 6 5 John Doe Ronald 6 4 6 5John Doe 21 7 4 6 14 John Doe 32 4 4 6 10 John Doe 32 6 4 6 10 John Doe32 6 4 6 10 Jon Doe Ron 39 1 8 6 3 12 Jon Doe 1 8 6

TABLE II.4C FN_Value LN_Value ST_Value CITY_Value FN LN ST DID CITYWeight Weight Weight Weight Jack Doe Florida 3 Tampa 8 6 8 9 Jack Doe 38 6 Jack Doe 3 8 6 Jason Doe Florida 5 5 6 8 Jason Doe 5 Orlando 5 6 4John Doe Florida 2 4 6 8 John Doe Florida 4 4 6 8 John Doe Florida 6 4 68 John Doe Florida 6 Tampa 4 6 8 9 John Doe Florida 6 4 6 8 John DoeFlorida 7 Orlando 4 6 8 4 John Doe Florida 7 4 6 8 John Doe 4 Tampa 4 69 John Doe 4 Tampa 4 6 9 John Doe 6 4 6 Jon Doe Florida 1 8 6 8 Jon Doe1 Miami 8 6 3

At block 215, a query associated with a record in a foreign database isreceived. Accordingly, the techniques of this section may proceed byreceiving a query that specifies or constrains at least one field value.Continuing the specific example under discussion, an exemplary query maybe of the form: {FN=John & MN=Ronald & LN=Doe & AGE=32 & CITY=Tampa &ST=Florida}.

At block 220, the query is compared to the plurality of distributedtables. An embodiment may proceed to perform a fetch operation for eachspecified search criterion that is associated with a fixed field if asearch criterion is provided for all of the fixed fields associated witha defined field match template. Since the given search criteria providesa search criterion for all the fixed fields of each defined field matchtemplate, the technique may proceed to perform a fetch operation foreach specified search criterion that is associated with a fixed field onone or more distributed tables associated with each defined field matchtemplate (e.g., Field Match Template A, Field Match Template B, FieldMatch Template C). In this instance, there are three fetches: oneperformed on Table II.4A that is associated with Field Match Template Afor FN=John & MN=Ronald & LN=Doe, one performed on Table II.4B that isassociated with Field Match Template B for FN=John & LN=Doe, and oneperformed on Table II.4C that is associated with Field Match Template Cfor FN=John & LN=Doe & ST=Florida. In various embodiments, one or morefetch operations may be performed in parallel using parallel processingtechniques described in FIGS. 1 and 2 of the '866 Applications.

The techniques of this section may continue by filtering the searchresults returned from each of the fetch operations using one or morespecified search criterion that are associated with optional fields.Accordingly, the technique may filter the search results returned fromthe first fetch operation on Table II.4A using one or more specifiedsearch criterion that are associated with optional fields of Field MatchTemplate A, the second fetch operation on Table II.4B using one or morespecified search criterion that are associated with optional fields ofField Match Template B, and the third fetch operation on Table II.4Cusing one or more specified search criterion that are associated withoptional fields of Field Match Template C.

Given that Field Match Template B includes two optional fields, thetechnique may filter the search results returned from the second fetchoperation on Table II.4B using the specified search criterion associatedwith the MN field and the AGE field of Field Match Template B.

As previously discussed, the filter operation may return a search resultfor a given search criterion that is associated with an optional fieldif the search criterion matches the field value of an optional field ofa record in the search results returned from the second fetch operationon Table II.4B or if the field value of the optional field of a recordin the search results returned from the second fetch operation on TableII.4B is blank (e.g., null value). Thus, the filter operation may filterthe search results returned from the second fetch operation on TableII.4B using MN=Ronald & AGE=32. In various embodiments, the filter mayreturn a search result for a given search criterion that is associatedwith an optional field of a record in the search results returned fromthe second fetch operation on Table II.4B if the search criterion isblank. Thus, the first fetch operation returns the eighth row thru theeleventh row of Table II.4A; the second fetch operation and the filteroperation returns the ninth row thru the eleventh row of Table II.4B andthe thirteenth row thru the fifteenth row of Table II.4B, and the thirdfetch operation returns the sixth row thru the twelfth row of TableII.4C. To illustrate, the Table II.5A resulting from the first fetchoperation, the Table II.5B resulting from the second fetch operation andthe filter operation, and the Table II.5C resulting from the third fetchoperation are produced below. It should be noted that, in someembodiments, records returned by such fetches and filters may be alteredto omit the actual field values. In such embodiments, the DIDs may beincluded.

TABLE II.5A FN_Value MN_Value LN_Value FN MN LN DID Weight Weight WeightJohn Ronald Doe 2 4 5 6 John Ronald Doe 4 4 5 6 John Ronald Doe 6 4 5 6John Ronald Doe 7 4 5 6

TABLE II.5B FN_Value LN_Value MN_Value AGE_Value FN LN MN AGE DID WeightWeight Weight Weight John Doe Ronald 2 4 6 5 John Doe Ronald 4 4 6 5John Doe Ronald 6 4 6 5 John Doe 32 4 4 6 10 John Doe 32 6 4 6 10 JohnDoe 32 6 4 6 10

TABLE II.5C FN_Value LN_Value ST_Value CITY_Value FN LN ST DID CITYWeight Weight Weight Weight John Doe Florida 2 4 6 8 John Doe Florida 44 6 8 John Doe Florida 6 4 6 8 John Doe Florida 6 Tampa 4 6 8 9 John DoeFlorida 6 4 6 8 John Doe Florida 7 Orlando 4 6 8 0 John Doe Florida 7 46 8

The technique may continue by merging the field value weights of thesearch results returned in Table II.5A, Table II.5B, and Table II.5C andgenerating a table of the largest field value weights for each non-DIDfield for each DID. The table may also include the total of the fieldvalue weights for each DID in the table. As previously discussed, and asillustrated in Table II.5A and Table II.5C, the search results returnedfrom the fetch operations may be sorted by DID since the DID isstrategically placed after the one or more fixed fields or one or moreoptional fields and before one or more extra credit fields. In variousembodiments, the search results illustrated in Table II.5B may be sortedby DID prior to being merged.

It should be noted that a search result may be returned for an extracredit field whether or not the given search criterion associated withthe extra credit field matches. In the event the given search criterionmatches a field value associated with an extra credit field of a recordin the database, the technique may count the field value weightassociated with the extra credit field value toward the match score. Inthe event the given search criterion does not match a field valueassociated with an extra credit field of a record in the database, thetechnique may not count the field value weight associated with the extracredit field value toward the match score and may return a zero for thefield value weight (e g., the CITY_Value weight associated with the“Orlando” field value is returned as zero in Table II.5C since the fieldvalue “Orlando” does not match the “Tampa” search criterion). (In someembodiments, if there is a mismatch between an extra credit field valueand the search criterion, then the field value weight for the extracredit field value is subtracted from the cumulative field value weighttotal.) Accordingly, in the record with DID 6, the extra credit fieldvalue (e.g., the city field value) may be counted towards the matchscore since the given criterion “Tampa” is a match. An exemplary tableis produced below.

TABLE II.6 FN_Value MN_Value LN_Value AGE_Value CITY_Value ST_Value DIDWeight Weight Weight Weight Weight Weight TOTAL 2 4 5 6 0 0 8 23 4 4 5 610 0 8 33 6 4 5 6 10 9 8 42 7 4 5 6 0 0 8 23

At block 225, an identifier for an entity representation is identifiedand outputted. Accordingly, the technique may output a DID thatcorresponds to the given search criteria (e.g., {FN=John & MN=Ronald &LN=Doe & AGE=32 & CITY=Tampa & ST=Florida}) using techniques and methodsdescribed in Section I. That is, the records of Table II.6 may be sortedaccording to total field value weight, and the techniques of Section Imay be applied to determine whether the first record matches the searchcriteria with a given confidence. If so, the associated DID may bereturned as responsive to the search criteria.

It should be noted that returning search results based on a given queryusing one or more field match templates may be processed in parallelsince a plurality of distributed tables associated with the one or morefield match templates may be generated, distributed, and stored over oneor more nodes according to techniques and methods described in referenceto FIGS. 1 and 2 of the '866 Applications. Accordingly, one or moresearch results may be fetched for each defined field match template inparallel.

According to an exemplary embodiment, a method for identifying an entityrepresentation associated with a universal database that corresponds toa query associated with a foreign database is disclosed. The methodincludes selecting one or more field match templates. The method alsoincludes providing the universal database, including one or moredistributed tables, each distributed table being associated with a fieldmatch template and storing one or more records sorted in a listaccording to one or more fields of the field match template, where eachrecord is associated with one or more entity representations. The methodfurther includes receiving a query associated with a record in theforeign database. The method further includes comparing the query to theone or more distributed tables to identify an entity representation inthe universal database that corresponds to the query. The method furtherincludes outputting the identified entity representation.

An optional feature of the above embodiment includes that the selectingis based on at least one or more query logs associated with theuniversal database, where each field match template includes at leastone of a fixed field portion, an optional field portion, and an extracredit field portion.

III. Batch Entity Representation Identification Using Field MatchTemplates

Techniques according to this section may match each record of a batchfile to an individual represented in a universal (or other) database.Inputs to an embodiment according to this section may include, but arenot limited to, a batch file and a universal (or other) database. Thebatch file may be part, all, or substantially all of a foreign (orother) database. An embodiment according to this section may compare therecords of the batch file to the records of the universal database, andattempt to create matches between the records in the batch file and theentity representations or records in the universal database. An outputof an embodiment according to this section may be a table that includesforeign record IDs of the batch file records, each in association withan entity representation of the universal database (e.g., using a DID).An embodiment according to this section may include a batch styleprocessing of the records.

The batch file may contain one or more records associated with a foreignrecord ID, and may be an entire foreign database, or may be comprised ofone or more records or one or more fields of the foreign database. Thebatch file may include records that are not complete (e.g., records thatdo not have data for every field), or erroneous (e.g., records that donot properly identify the same individual may, in fact, correspond tothe same individual). Each record in the batch file may be associatedwith a foreign record ID. Non-limiting examples of such foreign recordIDs are the RIDs discussed in the First Generation Patents AndApplications.

The universal database may contain one or more records, each associatedwith a DID. The universal database, as with the batch file, may includerecords that are not complete, or may be erroneous. A furtherdescription of an exemplary universal database is provided in Section I,above.

The fields in the batch file may be compared to the one or more fieldmatch templates (discussed, e.g., above in Section II). If an entry(e.g., record) in the batch file does not have a corresponding field fora field match template field designated as fixed, then that entry may bediscarded or ignored. As an example, in an exemplary first field matchtemplate containing fixed fields for first name (“FN”) and state (“ST”),an exemplary second field match template containing fixed fields forlast name (“LN”) and ST, and an exemplary third field match templatecontaining fixed fields FN and LN, if the batch file has correspondingfields for FN and LN, but does not have a corresponding field for ST,the exemplary first field match template and the exemplary second fieldmatch template may be discarded or ignored. In an alternate embodiment,the field match templates may be used, but if the batch file does nothave a corresponding field for a field match template designated asfixed, that field may be ignored or temporarily or permanently removedfrom the field match template.

Turning now to FIG. 3 a, an exemplary flowchart 300 is shown depictingan embodiment of an invention of this section. One or more hardwarenodes may be provided. The nodes may be as described in Section II,above, or may be as described in the First Generation Patents AndApplications. A master node may be provided, which may control orotherwise provide instruction to the nodes. Shown in block 301, themaster node may receive or be associated with the universal database.For example, the master node may be able to access and process theuniversal database. Shown in block 303, the master node may similarly beable to access and process the batch file.

The nodes may locally store one or more parts of the universal database,the batch file, or both. For example, each of the nodes may store one ormore portions of the universal database related to each of the fieldmatch templates. The master node may initially distribute the universaldatabase according to the methods described in the First GenerationPatents And Applications, or the universal database may be distributedin another way. The master node may distribute the universal database bytaking into account the fixed or optional fields in the field matchtemplate, so that searching or sorting may be executed across the nodesin a balanced or parallel manner. The distribution may occur before orafter partitioning the universal database into one or more sections atone or more partition points, which may take into account thedistribution of data according to a specific field match template. Thepartition points of the universal database may be different for each ofthe one or more field match templates. The individual nodes may createsuggested partition points, based in part on the part of the universaldatabase that is stored within the node. The nodes may transmit thesuggestions to the master node, and the master node may create partitionpoints based on the suggestions. The nodes may receive the partitionpoints from the master node, and may reply with other suggestedpartition points, iterating the process one or more times.

Shown in block 305, once the appropriate field match template ortemplates are chosen, the partition points selected for the recordscontained within the universal database for one of the appropriate fieldmatch templates are utilized to partition the records from the universaldatabase into one or more parts. Shown in block 309, the master node maytransmit the partition points or information embodying the partitionpoints to one or more of the one or more nodes. The one or more nodesmay utilize the partition point information provided by the master nodeto communicate with one or more of the one or more other nodes, and maytransfer portions of the universal database between themselves so thateach node has a part of the universal database according to thepartition points.

Shown in block 307, the master node and/or the nodes may also partitionthe batch file according to partition points. Such partition points maybe, by way of non-limiting example, based on the first letter of a lastname field (e.g., A-I, J-O, and P-Z). Other partition points based onother field values or criteria may be used in addition or in thealternative. In one exemplary embodiment, the partition points selectedfor the batch file are the same as the partition points selected for theuniversal database. The batch file may be partitioned into one or moreparts according to the partition points, and, shown in block 311, themaster node may transmit one or more parts of the batch file to one ormore of the one or more nodes. The same partition points may be selectedfor partitioning both the universal database and the batch file so that,for example, a part of the batch file may contain records likely to bematched to the corresponding part of the universal database. In thisway, the records to be matched between the batch file and the universaldatabase may reside on the same node. Instead of attempting to searchthe entire universal database for records matching a specific record inthe batch file, it may suffice to search a smaller part of the universaldatabase. The universal database and the batch file may also exist onone node or master node, and may not be partitioned. The partitioning ofthe batch file and the universal database into the one or more nodes maybe depicted as, by way of non-limiting example, FIG. 3 b.

For purposes of discussion, a specific, non-limiting example of auniversal database is presented below as depicted in Table III.1.

TABLE III.1 DID FN MN LN AGE CITY ST 1 Jon Ron Doe 39 Miami 1 Jon DoeFlorida 2 John Ronald Doe Florida 3 Jack Ron Doe 72 3 Jack Ronald DoeTampa Florida 3 Jack Doe 72 4 John Ron Doe Tampa 4 John Doe 32 Florida 4John Ronald Doe Tampa 5 Jason Rick Doe 31 Orlando 5 Jason Doe 31 Florida6 John Doe 32 6 John Ron Doe Florida 6 John Doe 32 Tampa Florida 6 JohnRon Doe Florida 7 John Ronald Doe 21 Orlando Florida 7 John Doe 21Florida 8 Jack Michael Lee 23 Orlando Florida 9 Jack Thomas Lee 39 TampaFlorida 89 Ron David Smith 39 Tampa Florida 90 Ron David Paul 20 TampaFlorida 91 David Joseph Smith Tampa Florida 91 David Smith Tampa Florida

Again for purposes of discussion, a specific non-limiting example batchfile is presented below as depicted in Table III.2.

TABLE III.2 Foreign Record ID FN MN LN AGE CITY ST 37 Jon Ron Doe 38John Ronald Doe Miami Florida 39 Jack Ronald Doe 40 Jon Doe 32 Florida54 Jason Doe 31 Orlando 68 John Ronald Doe 85 Jack Mike Lee OrlandoFlorida 96 Jack Thomas Lee 39 Florida 784 Ron Paul Tampa 785 DavidJoseph Smith Tampa Florida 786 David Jackson Smith Tampa Florida

For purposes of discussion, three field match templates are presented:Field Match Template A: (FN, MN, LN, DID) where all the non-DID fieldsof Field Match Template A are fixed fields; Field Match Template B: (FN,LN, MN, AGE, DID) where the first name field and the last name field ofField Match Template B are fixed fields and the middle name field andthe age field of Field Match Template B are optional fields, and FieldMatch Template C: (FN, LN, ST, DID, CITY) where the first name field,last name field, and state field of Field Match Template C are fixedfields and the city field of Field Match Template C is an extra creditfield.

According to the provisions of the First Generation Patents AndApplications, any, or a combination, of the master node and the othernodes may set partition points for the universal database so that theuniversal database is divided into one or more parts, as depicted below.For example, the universal database and the batch file may bepartitioned according to the “LN” field. FIGS. 11A, 11B and associatedtext of U.S. Pat. No. 7,293,024 to David Bayliss, et al. entitled“Method and System for Sorting and Distributing Data Among a Pluralityof Nodes,” issued Nov. 6, 2007, incorporated by reference herein show,in part, an embodiment of a method to partition data among one or morenodes. The universal database may be partitioned according to the one ormore fields designated as fixed fields in a particular field matchtemplate. From Table III.1, the universal database may be partitionedaccording to last name, yielding the following three parts:

TABLE III.3 DID FN MN LN AGE CITY ST 1 Jon Ron Doe 39 Miami 1 Jon DoeFlorida 2 John Ronald Doe Florida 3 Jack Ron Doe 72 3 Jack Ronald DoeTampa Florida 3 Jack Doe 72 4 John Ron Doe Tampa 4 John Doe 32 Florida 4John Ronald Doe Tampa 5 Jason Rick Doe 31 Orlando 5 Jason Doe 31 Florida6 John Doe 32 6 John Ron Doe Florida 6 John Doe 32 Tampa Florida 6 JohnRon Doe Florida 7 John Ronald Doe 21 Orlando Florida 7 John Doe 21Florida

TABLE III.4 DID FN MN LN AGE CITY ST 8 Jack Michael Lee 23 OrlandoFlorida 9 Jack Thomas Lee 39 Tampa Florida

TABLE III.5 DID FN MN LN AGE CITY ST 89 Ron David Smith 39 Tampa Florida90 Ron David Paul 20 Tampa Florida 91 David Joseph Smith Tampa Florida91 David Smith Tampa Florida

Table III.3 depicts an exemplary first part of a partition of theuniversal database depicted in Table III.1; Table III.4 depicts anexemplary second part of the same partition of the universal databasedepicted in Table III.1, and Table III.5 depicts an exemplary third partof the same partition of the universal database depicted in Table III.1Node a, node b, and node c may rearrange the data contained within theindividual nodes so that node a may contain the records of the firstpart of the universal database, node b may contain the records of thesecond part of the universal database, and node c may contain therecords of the third part of the universal database.

The master node may also partition the batch file into one or more partsusing the partition points created and used for the universal databaseand the field match template. The partitioning of the batch file mayyield the following three parts:

TABLE III.6 Foreign record ID FN MN LN AGE CITY ST 37 Jon Ron Doe 38John Ronald Doe Miami Florida 39 Jack Ronald Doe 40 Jon Doe 32 Florida54 Jason Doe 31 Orlando 68 John Ronald Doe

TABLE III.7 Foreign record ID FN MN LN AGE CITY ST 85 Jack Mike LeeOrlando Florida 96 Jack Thomas Lee 39 Florida

TABLE III.8 Foreign record ID FN MN LN AGE CITY ST 784 Ron Paul Tampa785 David Joseph Smith Tampa Florida 786 David Jackson Smith TampaFlorida

Table III.6 is an exemplary first part of the batch file depicted inTable III.2; Table III.7 is an exemplary second part of the batch filedepicted in Table III.2, and Table III.8 is an exemplary third part ofthe batch file depicted in Table III.2. Node a, node b, and node c mayrearrange the data contained within the individual nodes so that node amay contain the records of the first part of the batch file, node b maycontain the records of the second part of the batch file, and node c maycontain the records of the third part of the batch file.

Within each node, the partition of the batch file may be joined with thepartition of the universal database. Each join may use the table orpartition generated by the field match template from the universaldatabase, so as to include the records from the batch file that do nothave null values in the fields which are denoted as fixed in therespective field match template. For example, in Table III.2 above,showing an exemplary batch file, and an exemplary field match templatecontaining fixed fields “FN” and “ST,” the following records may beselected for the join:

TABLE III.9 Foreign record ID FN MN LN AGE CITY ST 38 John Ronald DoeMiami Florida 40 Jon Doe 32 Florida 85 Jack Mike Lee Orlando Florida 96Jack Thomas Lee 39 Florida 785 David Joseph Smith Tampa Florida 786David Jackson Smith Tampa Florida

Shown in block 315, the output of the join may include a tableassociated with each node containing an entry for each of the records inpart of the batch file associated with the node. The entries may containthe foreign record ID from the batch file, the DID from the universaldatabase, and the score for each of the fields in the field matchtemplate. The table may be sorted according to DID and then foreignrecord ID, and the nodes may transmit records between themselves so thatrecords for a given foreign record ID and DID are on the same node.Cumulative scores for the records may be calculated as discussedelsewhere herein. In the example shown below, a selection of exemplaryrecords in the table created for the join of the first part of theuniversal database partition and the first part of the batch filepartition on node a against a search criterion using techniques andmethods described in Section I may appear as depicted in Table III.10.Note that all possible matches to the universal database are not shown;only a subset of the matches, including non-exclusive and exemplarymatches for foreign record ID fields 37 and 38, are shown for exemplarypurposes only.

TABLE III.10 Foreign MN LN AGE CITY ST record ID DID FN Weight WeightWeight Weight Weight Weight Score 37 1 5 6 4 0 0 0 15 37 1 5 0 4 0 0 0 937 2 0 0 4 0 0 0 4 37 4 0 6 4 0 0 0 10 37 4 0 0 4 0 0 0 4 37 4 0 0 4 0 00 4 38 2 5 6 4 0 0 3 18 38 6 5 0 4 0 0 0 9 38 6 5 0 4 0 0 3 12 38 6 5 04 0 0 3 12 38 6 5 0 4 0 0 3 12

The resulting records from each of the nodes may be re-partitionedacross the nodes. The partition points may be determined by, forexample, the foreign record ID. The new partitions may allow for abalanced processing and matching of the records. For example, bypartitioning the records so that records having the same foreign recordID are on the same node, the nodes may be able to process the recordswithout having to query other nodes for additional records. Shown inblock 317, the resulting records having identical DID and foreign recordID fields may be consolidated or rolled-up, so that the field valueweights for each of the fields are combined, creating a single recordwith a DID and foreign record ID, and combined scores for each of thefields. That is, the records may be merged according to DID. Thecombination may be an operation to take the highest weight value in eachfield to become the weight value of the field aggregation, or may be inthe form of a summation, or may be an averaging of the records havingidentical DID and foreign record ID fields, or may be anothermathematical operation to aggregate the records having identical DID andforeign record ID fields. The exemplary records returned from the joinof the first part of the universal database and the first part of thebatch file shown in Table III.10 may be combined so that the highestfield value weight for each field for the records having identical DIDand foreign record ID fields becomes the field value weight for thefield of the combination. A portion of the resulting data is depicted inTable III.11, below.

TABLE III.11 Foreign MN LN AGE CITY ST record ID DID FN Weight WeightWeight Weight Weight Weight Score 37 1 5 6 4 0 0 0 15 37 2 0 0 4 0 0 0 437 4 0 6 4 0 0 0 10 38 2 5 6 4 0 0 3 18 38 6 5 0 4 0 0 3 12

The scores of each of the records returned from the table may then beconsidered to find probable matches according to the techniques ofSection I, above. That is, within each set of records bearing the sameforeign record ID, the techniques discussed above in relation toEquations 1-4 may be applied to decide whether the first record (sortedaccording to score) matches the search criteria with a known level ofconfidence.

Shown in block 319, the results from each node may be transmitted to themaster node for further processing, or each node may output the resultsto a user. The master node may collect the tables from each of thenodes. The tables may be concatenated to form a larger table. Theseresults may then be output to a user.

The above example is one embodiment of the techniques described in thissection. Other embodiments may also utilize the techniques described inthis section. For example, instead of distributing a universal databaseand a batch file to one or more nodes, a single node or system may beutilized to sort, merge, score, and/or output a combination of theuniversal database and the batch file. In another embodiment, if eitherthe universal database or the batch file, or both, were not in arelational database model, and one or more field match templates werespecified having at least one fixed field and zero or more optionaland/or extra credit fields, the techniques described in this section maybe utilized to sort the universal database or universal file by thefixed fields and the optional fields. The technique may then be used tosort the batch file according to the same fixed fields and optionalfields, if the fixed fields and the optional fields exist in the batchfile. If one or more of the fixed fields and/or optional fields do notexist in the batch fie, the technique may be operable to ignore thosefields in the field match template. The technique may process theuniversal database or universal file and the batch file sequentially,and may output a record in the universal file and a record in the batchfile if the field values match according to the field value template.For example, a record in the universal file and the batch file may beoutput when the field values of the fields corresponding to the fixedfields of the field match template of the universal file match similarfields from the batch file. The technique may then filter the resultantrecord pairs according to the optional field or fields in the fieldmatch template. The fields that are output may be scored according tothe techniques shown in sections I and II, above, and ordered by foreignID and DID.

According to an exemplary embodiment, a method for comparing recordsfrom a batch file to a universal database is disclosed. The methodincludes providing a batch file, including one or more records, eachrecord associated with a foreign record identification. The method alsoincludes providing a universal database, including one or more recordsordered so that the one or more records each are associated with one ormore entity identifiers, each entity identifier associated with anindividual. The method further includes comparing the batch file to theuniversal database to identify records. The method further includescreating a list of foreign record identifications, each associated withan entity identifier from the universal database and a linking score.

Various optional features of the above embodiment include the following.The method may include partitioning the universal database into a firstplurality of parts across one or more nodes according to one or morepartition points. The method may include partitioning the batch fileinto a second plurality of parts across one or more nodes according toone or more partition points.

IV. Method Of Partitioning Match Templates

Techniques according to this section may be used to determine how toaccount for field matches between given search criteria and records in adatabase. Such techniques allow for fuzzy matching of the given searchcriteria to the database being searched. Certain techniques according tothis section may use match templates to partition a given searchcriteria into (1) fields that must be populated and match, referred toas “fixed” fields, (2) fields that must match if populated, referred toas “optional” fields, and (3) fields that need not match, but that arecounted toward a match score if populated and a match or non-matchoccurs, referred to as “extra credit” fields. Section II containsfurther discussion of match templates and their field designations. Thetechniques of this section may be applied to the techniques of SectionsII and III in order to determine where to partition a given matchtemplate into fixed, optional and extra credit fields. Although thetechniques of this section are not limited to embodiments of thetechniques presented in Sections II and III, the following discussionis, by way of non-limiting example, presented in reference to thosesections.

In general, the techniques of this section may be applied to improvesearch processing speed. For example, the techniques of this section maybe used to partition a given match template into fixed, optional andextra credit portions so as to establish bounds on data processing andtransfer in performing a search. In general, for a given match templateand search criteria, as more of the match template is designated asfixed, fewer records in the database will match. That is, designating agreater portion of a match template as fixed may reduce the number ofrecords that are returned for further processing (e.g., processing oneor more optional or extra credit search field values). Processing timefor fixed fields is relatively short, as a single fetch may suffice toidentify all records that match in the fixed field portion of a searchtemplate. In contrast, in some embodiments, the optional field portionof a match template may not be amenable to a single simple fetchcommand. Thus, in some embodiments, each record that has been determinedto match the given search criteria in the fixed fields of the matchtemplate may be compared to the optional field values specified by thematch template and search criteria. Accordingly, as more fields arespecified as fixed, fewer comparisons are required for processing theoptional fields, thus reducing the comparison computational burden.Records that match according to the optional search criteria (e.g.,either the associated record field and search criterion match or one orboth are null) are output for further processing of extra credit fields(if any). In some embodiments, each of the records returned from theoptional field comparison operation are transferred for furtherprocessing, including the extra credit field values of those records.Thus, as less of a match template is designated as optional, more of thematch template may be designated as extra credit, and more records willgenerally be returned and possibly be transferred between computationresources. Accordingly, each portion of a match template may implicatedifferent amounts and types of processing. Techniques according to thepresent section may be used to designate fields in a match template asfixed, optional and extra credit in order to optimize or improveprocessing speed, reduce processing power, and reduce the number ofrecords transferred between computational portions of a given hardwaresystem.

Field probabilities and techniques for their computation are disclosedin the Second Generation Patents And Applications. In general, a fieldprobability for a given field may be interpreted as a measure ofdiversity of field values that appear in the given field. That is, for agiven database that includes a plurality of entity representations (orrecords), a field probability provides a measure of diversity of thefield values that appear in the associated field among the entityrepresentations (or records). (As discussed at length in the SecondGeneration Patents And Applications, a database may contain a pluralityof records, entity representations, or a combination thereof. By way ofillustration, the following discussion will utilize the term recordswithout limiting the scope of application of the techniques of thissection. That is, the techniques of this section may be applied todatabases containing entity representations.) A field probabilityassociated with a particular field in a record chosen at random from adatabase provides a probability that another randomly selected recordwill share the same field value in the particular field. Accordingly,the number of records in the database multiplied by a given fieldprobability provides an estimate of the number of records in thatdatabase that are expected to include the same field value in theassociated field as a record selected at random from the database. Putanother way, the number of records in the database multiplied by a givenfield probability provides the expected size (i.e., expected value ofthe size) of a field value cohort of a record chosen at random.

As discussed in detail in the Second Generation Patents AndApplications, the field probabilities (and field value probabilities)are each associated with a field weight (respectively, field valueweight). Further, as discussed in detail in the Second GenerationPatents And Applications, field weights and field value weights may beused instead of field probabilities and field value probabilities inorder allow for certain computations to be performed additively insteadof multiplicatively. Thus, field weights may be used according to thetechniques of this section to estimate expected database portion sizesin a manner discussed above.

Techniques according to this section may utilize field weights to selectportions of match templates as fixed, optional and extra credit. Moreparticularly, by utilizing field weights according to the techniquespresented in this section, the expected number of records returned ascomplying with each type of match template portion may be estimated.This estimation may be represented as, by way of non-limiting example:

w _(cumulative)=log(U)−log(S).   Equation 3

In Equation 3, w_(cumulative) represents an approximate bound on thecumulative sum of the field weights of the initial fields in a matchtemplate in order to limit the number of matching records toapproximately S, where U is the size of the database. A specific exampleof applying the technique described above follows.

is a flowchart depicting an embodiment of an invention of Section IV. Adiscussion of an embodiment of the technique of this section in relationto a particular non-limiting match template and other parametersfollows. At block 405, the relevant databases are identified. At block410, a match template is received. Suppose by way of non-limitingexample that the match template specifies the following fields in order:first name, last name, state, age and sex. In symbols, the matchtemplate may be represented as, by way of non-limiting example: (FN, LN,ST, AGE, SEX). Suppose further that each field has an associated fieldweight as presented in the following table.

TABLE IV.1 Field FN LN ST AGE SEX Field Weight 9 11 4 6 1

Again by way of non-limiting example, the match template may bepartitioned with the fixed fields appearing first, followed by theoptional fields, and then lastly the extra credit fields. In order todecide which fields should be declared as fixed, a user may specify arough limit on the number of records in the database that are expectedto match the fixed fields. That is, a user may specify a certain numberof records that are expected to match fields declared as fixed, and thetechnique under discussion will output which fields should be declaredas fixed in order to do so. For purposes of discussion, for theremainder of this example, the database will be assumed to contain onebillion (“1B”) records. Now suppose that, per block 415, it is desiredthat about 1024 records should be returned as matching the searchcriteria in the fixed fields. At block 420, the cumulative field weightsfor the fixed fields are calculated. In the present example, thecumulative field weights for the fixed fields should sum to no morethan, by way of non-limiting example, log(1B)−log(1024)=30−10=20. As thefield weights for the first two fields (FN and LN) sum to 20, these twofields may be declared as fixed. Accordingly, once the first two fieldsare selected as fixed, the number of records that may have theiroptional fields compared to the optional fields of the search criteriawill be expected to be 1024. Thus, selecting a rough bound on the numberof records that are expected to match in the fixed fields allows forplacing a rough limit on the number of records whose optional fieldswill be compared to the optional fields of the search criteria, therebylimiting the expected comparison computational resources utilized.

At block 425, in order to decide which fields should be declared asoptional, a user may specify a rough limit on the number of records inthe database that are expected to match the optional fields. That is, auser may specify a certain number of records that are expected to matchfields declared as optional, and the technique under discussion willoutput which fields should be declared as optional in order to do so.Now suppose that it is desired that about 16 records should be returned,on average, as matching the search criteria in the fixed and optionalfields. At block 430, the cumulative field weights for the fixed andoptional fields are calculated. In the present example, the cumulativefield weights for the fixed and optional fields may sum to no more than,by way of non-limiting example, log(1B)−log(16)=30−4=26. Because the sumof field weights for the first three fields of the match template (FN,LN, ST) is 24, which is less than 26, and because the sum of the fieldweights of the first four fields of the match template (FN, LN, ST, AGE)is 30, which is more than 26, the third field should be declared asoptional. Accordingly, by declaring that the third field is optional,the number of records that are expected to match in the fixed andoptional fields is about 16. Thus, the number of records that may betransferred between computational resources is limited by declaring thatthe third field is optional.

At block 435, the remaining fields of the match template may be declaredextra credit. To conclude the example, once the first two fields of thematch template have been declared fixed and the third field has beendeclared optional, the remaining fourth and fifth fields may be declaredas extra credit. By limiting the fixed fields to the first two fields,the amount of computational comparisons is limited to about 1024. Bylimiting the optional field to the third field, the number of recordsthat may be transferred is limited to about 16. Thus, by selectingapproximate bounds on the number of records that are expected to matchthe fixed fields and the optional fields of a match template, thetechniques of this section may be used to determine which fields in thematch template should be declared as fixed, optional and extra credit inorder to meet the selected bounds.

At block 440, the match template partition is output. The output may beto a user in a human readable form. Alternately, or in addition, thematch template partition may be output to another computer in computerreadable form. Alternately, or in addition, the match template partitionmay be transferred from one program module to another program modulewithin the same computer or computer network. The receiving computer orprogram module may use the match template partition to identify entityrepresentations as discussed in, e.g., Sections I-III of the presentdisclosure.

In some embodiments, the cumulative sums of the field weights in a givenmatch template may be allowed to exceed the limit calculated accordingto Equation 3. In such embodiments, the first field whose weight causesthe cumulative weight to exceed the calculated limit is declared asbeing in the fixed (respectively, optional) match template portion. Insome embodiments, the cumulative sum that is nearest to the calculatedlimit is used to determine the included field. In such embodiments, if acumulative sum is less than the calculated limit by a first number, andif including another field weight in the cumulative sum causes thecumulative sum to exceed the calculated limit by a second number, theassociated field may be included or not in the fixed (respectively,optional) fields of the match template according to whether the firstnumber is greater than or less than the second number.

In some embodiments, the techniques of this section may be applied tomultiple match templates as part of the same process.

V. Statistical Measure and Calibration of Internally Inconsistent SearchCriteria Where One or Both of the Search Criteria and Database isIncomplete

Techniques according to this section may he used to determine whetherthere is a match to a given search criteria, where the given searchcriteria may contain two or more different field values for the samefield. A search criteria that specifies two or more different fieldvalues for a single field is referred to herein as “internallyinconsistent.” Such search criteria may arise in a variety ofsituations. For example, such a search criteria may specify both amaiden and a married last name. As another example, an internallyinconsistent search criteria may specify two different addresses for thesame person, where it is suspected that the person has lived at bothaddresses at one time or another. In general, an information that maychange for an individual may give rise to internally inconsistent searchcriteria. Techniques according to the present section may be used toprocess and determine matches for internally inconsistent searchcriteria.

The technique of the present section may be used in conjunction with atechnique of any of other section included in this disclosure. Inparticular, the techniques of this section may be used as part of asearch technique disclosed in any of Sections I, II or III. However, thetechniques of this section are not limited to implementation inembodiments disclosed herein.

FIG. 5 is a flowchart depicting an embodiment of an invention of thissection. By way of non-limiting example, the embodiment is presentedrelative to the techniques of Section II; however, this presentation isexemplary only and not meant to be limiting. The present technique maybe used with any of the embodiments discussed in Sections I-III, or withother embodiments. The discussion will proceed relative to thetechniques of Section II, by way of non-limiting example. An embodimentaccording to this section is capable of accurately processing queriesthat include two (or more) different field values for a single field.(Embodiments allow for this to occur in more than one field; that is,more than one field value may be specified for more than one field.) Thetechnique proceeds to generate results tables as discussed above.However, the results tables will include fields configured to containthe field values associated with the field value weights when more thanone is specified in a search criteria. When the results are mergedaccording to DID (or other individual identifier), both of the weightsare taken into account by cumulative addition if they have not alreadybeen accounted for. Thus, the internally inconsistent search criteriaare accounted for by both weight and field value.

More particularly, when merging a record into a cumulatively mergedrecord, a decision is made as to whether a particular field value hasalready been accounted for in the cumulatively merged record. If so, thefield value in the record to be merged has already been accounted forand therefore need not be merged. If not, then the field value weight isadded to the cumulative sum and the field value is added to a list inthe merged record that tracks which field values have been accountedfor.

Relative to FIG. 5, a specific, non-limiting example is discussedpresently. At block 505, a database in which the search will beconducted is identified. Such a database may be a universal database asdiscussed elsewhere herein. For purposes of discussion, a portion of adatabase with records containing a first name field (“FN”), a middlename field (“MN”), a last name field (“LN”), an age field (“AGE”), acity field (“CITY”), and a state field (“ST”) is presented below.

TABLE V.1 DID FN MN LN AGE CITY ST 1 Jane Chris Smith 40 Vero FL 1 JaneChris Doe Vero FL 2 Jane Chris Smith 21 FL 2 J. C. Smythe 3 Jane Doe FL4 John David Doe FL

At block 510, one or more match templates is received. Again for thepurposes of discussion and without limitation, three match templates maybe used in this example:

-   -   (A) FN, MN, LN, where all fields are fixed;    -   (B) FN, LN, AGE, where FN and LN are fixed and AGE is optional;        and    -   (C) LN, CITY, ST, where LN is fixed, CITY is optional and ST is        extra credit.

At block 515, search criteria are received. Continuing this example,suppose that it is desired to search for a 40-year-old individual livingin Vero, Fla. whose maiden name used to be Jane Chris Smith and whosemarried name is now Jane Chris Doe. The following search criteria may beused: FN=Jane, MN=Chris, LN=Smith, LN=Doe, AGE=40, CITY=Vero, ST=FL.Now, according to the techniques of Section II, each match template isused to compare the search criteria to the records in the database,represented here as Table V.1. Thus, at block 520, match tables areproduced. The match templates accordingly produce the following tables(all weights are field value weights):

TABLE V.2 FN MN LN LN Field DID Weight Weight Weight Value 1 7 5 6 Smith1 7 5 7 Doe 2 7 5 6 Smith

Table V.2 corresponds to match template (A) above.

TABLE V.3 FN LN LN Field AGE DID Weight Weight Value Weight 1 7 6 Smith17 1 7 7 Doe 3 7 7 Doe

Table V.2 corresponds to match template (B) above.

TABLE V.4 LN LN Field CITY ST DID Weight Value Weight Weight 1 6 Smith 95 1 7 Doe 9 5 2 6 Smith 5 2 6 Smith 3 7 Doe 5 4 7 Doe 5

Table V.4 corresponds to match template (C) above. At block 525, thematch tables are joined according to entity representation. Accordingly,Tables V.1, V.2 and V.3 are merged to yield, by way of non-limitingexample:

TABLE V.5 FN MN LN LN AGE CITY ST Total DID Weight Weight Weight FieldValue Weight Weight Weight Weight 1 7 5 13 Smith, 17 9 5 56 Doe 2 7 5 6Smith 5 23 3 7 7 Doe 5 19 4 7 Doe 5 12

At block 530, the weights are summed according to entity representation.Note that in Table V.5, once the two field values of “Smith” and “Doe”have been accounted for by inclusion into the LN Field Value field andby the sum of the associated field value weights appearing in the LNWeight field, these values need not be further accounted for. Thus, thetwo field values and weights are accounted for with the merging of TableV.2 according to DID. The LN field values of Tables V.3 and V.4 need notbe additionally added. Next, at block 535, the table is sorted accordingto total weight. Because Table V.5 is already sorted by weight, theresults remain the same in this example:

TABLE V.6 FN MN LN LN AGE CITY ST Total DID Weight Weight Weight FieldValue Weight Weight Weight Weight 1 7 5 13 Smith, 17 9 5 56 Doe 3 7 5 7Doe 5 23 2 7 6 Smith 5 18 4 7 Doe 5 12

At block 540, a confidence level of the accuracy of the highest rankedentity representation is assessed. Thus, the techniques of Section I maybe applied to determine whether the first ranked record is indeed thecorrect record. For example, employing the techniques discussed inrelation to Equation 1, the difference between the total weight for thefirst two records is 33, which is greater than, for example,−log(1−99.999%)=16. Therefore, according to the techniques of Equation1, the first record in Table V.6 is the correct record with a confidencelevel of at least 99.999%.

At block 545, an identifier of the identified entity representation isoutput if the confidence level is sufficient. The output may be to auser in a human readable form. Alternately, or in addition, the entityrepresentation identifier may be output to another computer in computerreadable form. Alternately, or in addition, the entity representationidentifier may be transferred from one program module to another programmodule within the same computer or computer network. Note that theentity representation identifier is not limited to a DID. Any identifiersufficient to identify the entity representation may suffice (e.g., asocial security number).

An second exemplary embodiment is discussed presently. This secondexemplary embodiment combines fuzzy matching techniques (e.g., those setforth in the Second Generation Patents And Applications in Section III)with the techniques of this section. For purposes of illustration ratherthan limitation, the second exemplary embodiment may utilize a symmetricand reflexive function (e.g., as discussed in Section III of the SecondGeneration Patents And Applications) to determine fuzzy matches. Inparticular, an edit distance function may be employed. Again forpurposes of illustration rather than limitation, Hamming distance,denoted by “D,” will be discussed in relation to the second exemplaryembodiment of this section.

The second exemplary embodiment proceeds similarly to the firstexemplary embodiment of this section, except that when combining recordswith the same DID (more generally, the same entity identifier), themaximum of the field value weights from among field values that liewithin the fuzzy match is taken. Field value weights for field valuesthat lie outside the fuzzy matching distance, but that match in theinternally inconsistent portion of the search criteria, are added. Thesefeatures are illustrated by a concrete example below.

For purposes of discussion of the second exemplary embodiment, a portionof a database with records containing a first name field (“FN”), amiddle name field (“MN”), and a last name field (“LN”) is presentedbelow.

TABLE V.7 DID FN MN LN 1 Jeff Clive Smith 1 Jeffrey Clive Smith 1 CliveSmith 2 Hans C. Anderson

Field value weights may be associated to each field value in the table,by way of non-limiting example, by adding additional fields. Inparticular, for each first name field value, a field may be added andpopulated with field value weights for field values that lie within anedit distance of three (3) of the first name field value as determinedby the edit distance function D. The resulting database portion may berepresented as, by way of non-limiting example (all weights are fieldvalue weights):

TABLE V.8 FN Weight For Edit Dis- tance FN MN LN Of DID FN MN LN WeightWeight Weight Three 1 Jeff Clive Smith 6 10 6 2 1 Jeffrey Clive Smith 810 6 3 1 Clive Smith 10 6 2 2 Hans C. Anderson 9 4 7 2

Continuing the discussion of the second exemplary embodiment, aninternally inconsistent search criteria may be formed as, by way ofnon-limiting example: {FN=Jeffrey & FN=Clive & LN=Smith}. For anexemplary match template of (FN, LN) with both fields optional (again byway of non-limiting example) and the exemplary internally inconsistentsearch criteria, the following table may be produced when the searchcriteria is applied to the database portion of Table V.7.

TABLE V.9 FN Weight For Edit DID FN Weight FN Field Value LN WeightDistance Of Three 1 Jeff 6 2 1 8 Jeffrey 6 3 1 10 Clive 6

The fourth column of Table V.9 reflects, among other things, that thestring “Jeff” is within an edit distance of three (3) of the strings“Jeff” and “Jeffrey”. The second exemplary embodiment may proceed tocombine the results reflected in Table V.9, as each result is associatedwith the same DID (more generally, the same entity identifier). Due tothe presence of the fuzzy matching parameters, this combination proceedsin a different manner than that of the first exemplary embodiment ofthis section. Specifically, field values that are within the specifiededit distance of each other are counted at most once, and the greatestfield value weight between such field values is taken. Any remainingfield value weights corresponding to matching field values that lieoutside the specified edit distance are added. Thus, combining the firstand second records reflected in Table V.9 above may yield, by way ofnon-limiting example:

TABLE V.10 FN Weight For Edit DID FN Weight FN Field Value LN WeightDistance Of Three 1 8 Jeffrey 6

Table V.10 reflects that although the first two records of Table V.9match the search criteria in the first name field, the first recordrequires the edit distance function to match, whereas the second recorddoes not. Accordingly, the field value weight for the exact match istaken instead of the field value weight for the fuzzy match. Combiningthe third record with the records combined thus far as reflected inTable V.10 yields, by way of non-limiting example:

TABLE V.11 FN Weight For Edit DID FN Weight FN Field Value LN WeightDistance Of Three 1 18 Jeffrey, Clive 6

Table V.11 reflects that for the first name match of “Clive” to theinternally inconsistent search criteria, which is not yet reflected inthe combined record of Table V.10 because “Clive” is not within an editdistance of three (3) of the FN field value already present in thecombined record, the field value weight for “Clive” is added to thecumulative field value weight (namely, 8) computed thus far.

The sum total field value weights of the combined record of Table V.8 is18+6=22. This score may be compared with other sum total field valueweights from other records (not shown in the example) using thetechniques of Section I in order to determine, with a known level ofconfidence, whether the records with DID of 1 in the database reflectedin Table V.7 do indeed match the given search criteria.

VI. Statistical Measure and Calibration of Reflexive, Symmetric andTransitive Fuzzy Search Criteria Where One or Both of the SearchCriteria and Database is Incomplete

Techniques according to this section may be used to identify anindividual in response to a query (e.g., by identifying a record orentity representation associated with such individual). Some embodimentsmay be implemented with respect to a database that contains a pluralityof records, entity representations, or a combination thereof.Embodiments of the techniques of this section may receive a query thatspecifies or constrains the field values for one or more fields. Inparticular, such embodiments may account for near matches in one or morefields, where a near match is defined by a reflexive, symmetric andtransitive relations, such as SOUNDEX. Such embodiments may proceed toidentify the record or entity representation that most likelycorresponds to individual identified by the query.

The present technique may use various measures of near match. That is,the present technique is not limited to a single measure of near matchesbetween field values. Instead, any reflexive, symmetric and transitivefunction may be used to detect or measure similarity of field values. Anexample of such a function is SOUNDEX. The SOUNDEX function takes astring as an argument and outputs a code in standard format thatprovides an indication of the string's pronunciation. The output of theSOUNDEX function (or any other reflexive, symmetric and transitivefunction) may be referred to herein as a “code.” Note that, in general,reflexive, symmetric and transitive functions define a partition of thedomain over which the function operates, where the partition may bedefined according to the codes assigned to elements of the domain by thefunction. That is, each part of the partition may be defined by adifferent code assigned only to the elements in that part by thefunction. The SOUNDEX function is reflexive because it produces the samecode every time the same string is input. It is symmetric because if twostrings produce the same code, they will produce the same coderegardless as to the order of computation, i.e., regardless as to whichstring is fed into the SOUNDEX function first. The SOUNDEX function istransitive because if a first string and a second string produce thesame code, and if the second string and a third string produce the samecode, then the first string and the third string produce the same code.

As another example, the first initial function is reflexive, symmetricand transitive. This function, denoted here by F(·), takes as an inputany string and outputs the first character of the string. Thus, forexample, F(Chris)=C. The first initial function is reflexive because itproduces the same code every time the same string is input. It issymmetric because if two strings produce the same code, they willproduce the same code regardless as to the order of computation, i.e.,regardless as to which string is fed into the first initial functionfirst. The first initial function is transitive because if a firststring and a second string produce the same code, and if the secondstring and a third string produce the same code, then the first stringand the third string produce the same code. Thus, the first initialfunction is another non-limiting example of a function that may beimplemented in the techniques of this section.

Note that the edit distance function is not transitive. For example, theedit distance between the strings “tape” and “tale” is one, and the editdistance between the strings “tale” and “tall” is one, but the editdistance between the string “tape” and “tall” is two, rather than one.

For the remainder of this section, the term D will denote a functionwith the appropriate properties, not limited to SOUNDEX or firstinitial. Note that unary functions or binary functions may be used withthe present technique.

Near matches in one or more selected fields may be accounted for byreplacing selected field values by codes generated by the function, withor without adding to each record new fields populated by the originalcontents of the selected field. Thus, in some embodiments, once thecontents of the selected field are converted to their correspondingcodes, the original contents of the selected field are added to a newfield in each record.

Thus, for example, two records in a database may originally appear as,by way of non-limiting example:

TABLE VI.1 First Name Last Name John Smiff Jon Smith

The last name may be selected for allowing for near matches. In such anexample, the field values that appear in the last name field may bereplaced with, by way of non-limiting example, SOUNDEX codes for theassociated field values. As the SOUNDEX code for “Smith” is S530 and theSOUNDEX code for “Smiff” is S510, the altered table may appear as, byway of non-limiting example:

TABLE VI.2 First Name Last Name John S510 Jon S530

In Table VI.2, the last name field values are replaced with theirSOUNDEX code. Any search criteria may thereafter be processed accordingto any of the techniques of Sections I-III. A near match betweenoriginal field values may be detected and accounted for by detecting anexact match between codes and processed as discussed in any of SectionsI-III.

FIG. 6 is a flowchart illustrating an exemplary embodiment of thissection. At block 605 a database is identified. At block 610 asymmetric, reflexive and transitive function is selected. At block 615,a field is selected, and the function is applied to the contents of suchfield in each record in the database. In this example, near matches inone or more selected fields are accounted for by conjoining to recordsone or more additional fields that store codes for the selected fields.Thus, at block 620, for the selected field, a corresponding additionalfield is appended to each record, and the contents of the selected fieldmay be transferred to the added field. At block 625, the contents of theoriginal field are be replaced by a code for the contents of theoriginal field. At block 630, the field value weight for the contents ofthe original field may be spread across the original field and the addedfield. More particularly, the field value weight for the replacementfield may be computed according to the codes contained therein, and thefield value weight for the appended field may then be computed as thedifference between the field weight for the original field and the fieldweight for the replacement field. These processes may be performed formore than one selected field. At block 635, a technique according toSection IV may be applied to compute (or re-compute) how a particularmatch template should be partitioned between fixed, optional and extracredit fields. At block 640, a search criteria is received, thetechniques of any of Sections I-III may be applied in order to generatea list of records arranged by cumulative weight, and any of thetechniques of Section I may be used to confirm that the highest-rankedrecord matches a given search criteria with a particular level ofconfidence. At block 645, an identifier of the highest ranked entityrepresentation is output if the confidence level is sufficient.

A specific example is provided to illustrate an application of atechnique according to the second exemplary embodiment. This example ispresented relative to a selected match template for first name (“FN”),last name (“LN”), state (“ST”), age (“AGE”) and sex (“SEX”). Thus, thematch template mat be represented as, by way of non-limiting example:(FN, LN, ST, AGE, SEX). For a particular record, the field value weightsfor this match template are represented in the table below.

TABLE VI.3 Field FN LN ST AGE SEX Field Value Weight 10 15 6 4 1

In this example, it is desired to allow for near matches in the lastname field. By way of non-limiting example, the selected reflexive,symmetric and transitive function that is used to gauge near matches maybe SOUNDEX. The match template may accordingly be altered by appending afield configured to include the contents of the original last namefield. The original first name field may be replaced by a field thatcontains a SOUNDEX code for last name. Thus, the altered match templatemay be represented as, by way of non-limiting example: (FN, LN_CODE, ST,AGE, SEX, LN). For the particular record, the field value weight for thefield value in the replacement field may be computed and associated withthe replacement field, and the field value weight for the field value inthe appended last name field may be computed by subtracting the fieldvalue weight of the field value in the replacement field from theoriginal field value weight for the field value in the last name field.For the particular record under discussion, the field value weights forthe altered match template are represented in the table below.

TABLE VI.4 Field FN LN_CODE ST AGE SEX LN Field Value Weight 10 8 6 4 17

Table VI.4 reflects that the field value weight for the last name codehas been computed as eight (8). In this embodiment, this number issubtracted from the original field value weight for the last name fieldvalue (15), yielding five (7). The new field value weight for the lastname field value is accordingly associated with the appended last namefield.

At this stage, the technique of Section IV may be applied. For purposesof illustration and discussion, the field weights presented in SectionIV in Table IV.1 are assumed to apply to the present example. Asdiscussed in Section IV, for the original search template of (FN, LN,ST, AGE, SEX) with field weights as presented in Table IV.1, the firsttwo fields were determined to be fixed, the third field was determinedto be optional, and the remaining fields were determined to be extracredit. These determinations were made according to specified parametersof one billion records in the database, 1024 records returned on averageas matching the search criteria in the fixed fields, and 16 recordsreturned on average as matching the search criteria in both the fixedand optional fields. Continuing the example of this section, forpurposes of discussion, it may be assumed that the field weight for theLN_CODE field is computed according to the techniques set forth in theSecond Generation Patents And Applications as six (6). Then the fieldweights for the altered match template may be represented as in TableVI.5 below.

TABLE VI.5 Field FN LN_CODE ST AGE SEX LN Field Weight 9 6 4 6 1 5

With the same parameters of one billion records in the database, 1024records returned on average as matching the search criteria in the fixedfields, and 16 records returned on average as matching the searchcriteria in both the fixed and optional fields, by applying thetechniques of Section IV to the altered match template of (SN, LN_CODE,ST, AGE, SEX, LN) with field values as reflected in Table VI.5, thefollowing fields should be declared as fixed: FN, LN_CODE, ST and AGE.Similarly, the SEX field should be declared as optional, and theoriginal LN field should be declared as extra credit. With this newmatch template, near matches in the last name field are accommodated inthe replacement LN_CODE field, and exact matches, should they occur, areaccounted for in the LN field appended at the end.

The revised match template (FN, LN_CODE, ST, AGE, SEX, LN) may then beused according to the techniques of any of Sections I-III in order toprocess a search criteria and locate a matching record with a specifiedconfidence.

In an alternate embodiment, for an original field and a field containinga code, the highest field value weight (or field weight) may be selectedfor the purpose of calculating a cumulative score for a match between agiven record and search criteria.

VII. Entity Representation Identification Using Entity RepresentationLevel Information

Techniques according to this section may be used to determine matchesbetween entity representations in a universal and a foreign database(more generally, between any two databases). Note that, in general, anentity representation is one or more linked records that correspond tothe same individual. Universal and foreign databases may contain entityrepresentations rather than solely unlinked records. Techniquesaccording to this section allow for matching one or more entityrepresentations in a foreign database to the corresponding entityrepresentation(s) in a universal database (more generally, and for theremainder of the discussion, between any two databases). Techniquesaccording to this section may utilize a foreign database's entityrepresentations, which generally contain more information than singleunlinked records, as part of the match process. Accordingly, techniquesaccording to this section may produce highly accurate results.

Exemplary techniques of this section may be applied to, for example, thetechniques of Sections I-III. The techniques of those sections may, insome embodiments, match a query based on a record in a foreign database(Section II) or batch file containing records from a foreign database(Section III) to one or more entity representations in a universaldatabase. According to the techniques of the present section, thetechniques of Sections I-III may be altered as discussed presently tomatch a query that defines an entity representation in a foreigndatabase (Section II) or a batch file that defines one or more entityrepresentations in a foreign database (Section III) to one or moreentity representations in a universal database, while taking intoaccount the entity representation structure present in the foreigndatabase. Although the techniques of this section are suitable foraltering the embodiments of Sections I-III to account for foreigndatabase entity representations, the techniques of this section are notso limited. That is, the techniques of this section may be applied tosearch techniques other than those presented in Sections I-III. Forpurposes of illustration rather than limitation, the techniques of thissection are presented in reference to the techniques of Sections II andIII.

Embodiments of this section as applied to the techniques of Section IIare discussed presently. Such embodiments may receive as an input anidentification of an entity representation in a foreign database andoutput an entity representation identification (e.g., a DID) for amatching entity representation in the universal database. That is, asapplied to the techniques of Section II, a query identifying an entityrepresentation in a universal database may be applied to a universaldatabase in order to identify the entity representation in the universaldatabase that matches the query. Moreover, the matching process may takeinto account the entity representation structure of the foreigndatabase. Two different but related techniques may be applied to thetechniques of Section II.

FIG. 7A is a flowchart depicting an embodiment of an invention of thissection. As a first exemplary embodiment according to the techniques ofthis section and Section II, the process may proceed as follows. Atblock 705A, the relevant foreign and universal databases (by way ofnon-limiting example) are selected. Block 710A proceeds by identifyingan individual reflected by at least one record in the foreign database.The process will output, with a known level of confidence, the DID orother identification of a corresponding entity representation in theuniversal database. Upon receiving an initial query or other searchcriteria specifying an entity representation in the foreign database,referred to herein as the “foreign entity representation,” the exemplaryembodiment proceeds, at block 715A, to generate a comprehensive query.The comprehensive query may specify all (or substantially all) featuresof the entity representation in the foreign database. In order to do so,the comprehensive query may be formed to include internally inconsistentsearch criteria as that term finds meaning in Section V above. Moreparticularly, the comprehensive query may include search criteria foreach field value that appears in the foreign entity representation,using, for example, the techniques of Section V if necessary. The querymay then be processed according to the techniques of Sections II and Vin order to identify a corresponding entity representation in theuniversal database. Thus, at block 720A, search results are generatedusing the aforementioned techniques, and at block 725A, the searchresults are ranked according to summed weight. At block 730A, anidentifier (e.g., a DID) of the highest ranked search result is outputif the confidence level is sufficient, as disclosed in Section I.

For example, a foreign entity representation may consist of thefollowing records:

TABLE VII.1 Foreign_DID Foreign_RID FN MN LN AGE CITY ST 7 126 Mary DoeNew York NY 7 12 Mary Doe 7 248 Mary Doe 40 FL 7 84 Mary Ann Smith TampaFL

As is apparent from an inspection of Table VII.1, each record bears thesame foreign DID, hence, each record corresponds to the same individual.Note further that each record has a different foreign recordidentification. In Table VII.1, the heading “FN” corresponds to thefirst name field, “MN” corresponds to the middle name field, “LN”corresponds to the last name field, “AGE” corresponds to the age field,“CITY” corresponds to the city field, and “ST” corresponds to the statefield. A user may input an initial query that specifies the foreignentity representation. By way of non-limiting example, the initial querymay be of the form: {Foreign_DID=7}, intended to identify the foreignentity representation depicted in Table VII.1. A comprehensive query maythen be constructed from the initial query. In this instance, thecomprehensive query may be of the form: {FN=Mary & MN=Ann & LN=Doe &LN=Smith & AGE=40 & CITY=New York & CITY=Tampa & ST=NY & ST=FL}. Notethat this query includes at least three internally inconsistent searchcriteria, namely, those for fields LN, CITY and ST, as the foreignentity representation depicted in Table VII.1 contains records withmultiple field values in these fields. The comprehensive query may thenbe processed according to the techniques of Section V (and a confidencecalculated according to the techniques of Section I) in order toidentify a matching entity representation in the universal database.Note that including, in the comprehensive query, every field value thatappears in any record of the foreign entity representation allows forall information of the foreign entity representation to be used indetecting a match. (Note that in some embodiments, a portion of, orsubstantially all field values are included.)

FIG. 7B is a flowchart depicting an embodiment of an invention of thissection. As a second exemplary embodiment according to the techniques ofthis section and Section II, the process may proceed as follows. Atblock 705B, the relevant foreign and universal databases (by way ofnon-limiting example) are selected. Block 710B proceeds by identifyingone or more individuals reflected by at least one record in the foreigndatabase. The second exemplary embodiment may be thought of asincorporating batch processing into the first exemplary embodiment.Thus, more than one initial query may be submitted. In some embodiments,the technique waits until several queries are submitted and thenprocesses them in batch mode. This may be accomplished by generating acomprehensive query for each initial query at block 715B, using, forexample, the techniques of Section V to account for inconsistent fieldvalues. At block 720B, these multiple comprehensive queries may beprocessed according to the techniques of Section II in parallel,simultaneously, sequentially, or a combination thereof, to generatesearch results. Each comprehensive query is processed according to thefirst exemplary embodiment, except that at block 722B, a queryidentifier is appended to each result in order to specify which query itis responsive to. That is, each result record may be amended to includea query identifier. In some embodiments, queries are grouped accordingto foreign entity representations, and each group is processed in thesame batch and given the same query identifier. The batch results maythen be combined according to any of the techniques presented inSections II or III above. At block 725B, the search results are rankedaccording to summed weight, and at block 730B, an identifier (e.g., aDID) of the highest ranked search result is output if the confidencelevel is sufficient, as disclosed in Section I.

FIG. 7C is a flowchart depicting an embodiment of an invention of thissection. That is, FIG. 7C depicts a third exemplary embodiment accordingto the techniques of this section and the techniques of Section III. Insuch an embodiment, one or more foreign entity representations areidentified, and the embodiment proceeds to provide a corresponding setof entity representations from the universal database, where eachidentified entity representation corresponds to an entity representationin the provided set. This process may proceed as follows. At block 705B,the relevant foreign and universal databases (by way of non-limitingexample) are selected. Block 710B proceeds by identifying one or moreindividuals reflected by at least one record in the foreign database.Once the foreign entity representations are identified (e.g., byidentifying a list of one or more foreign DIDs), the embodiment mayproceed to gather all foreign records that correspond to each foreignentity representation. Thus, for each identified foreign entityrepresentation, all included records are grouped together to generatecomprehensive search criteria at block 715C. Each of these groups may beincluded in a batch file, modified to distinguish the groups, andprocessed according to a technique of Section III. The modification mayinclude adding a foreign entity representation identification (e.g., aforeign DID) to each record (block 717C) and is intended to allow eachforeign entity representation to be handled as a group according to thetechniques of Section III (block 720C). In some embodiments, each groupof records that corresponds to the same foreign entity representation ismodified by, in each record in the group, replacing the foreign recordidentification with the foreign DID. The foreign DIDs may then behandled according to the techniques of Section III as if they wereforeign record identifications. The techniques of Section III may thenbe applied to identify the leading candidates from the universaldatabase that correspond to each foreign record. At block 722C, thesearch results are each associated with an identifier of the searchcriteria for which they are associated. At block 725C, each set ofsearch results is ranked according to summed weight, and at block 730C,an identifier (e.g., a DID) of the highest ranked search result for eachset is output if the confidence level is sufficient, as disclosed inSection I.

For purposes of discussion and by way of non-limiting example, the thirdexemplary embodiment is applied here to the example presented in SectionIII. Table III.2 is modified by replacing foreign record IDs withforeign DIDs. Assuming for purposes of illustration that the recordsappearing in Table III.2 having foreign record IDs 37-40 and 68 areassociated with a foreign entity representation having a foreign DID of1, the record with foreign record ID of 54 is associated with a foreignentity representation having a foreign DID of 2, the records withforeign record IDs of 785 and 786 are associated with a foreign entityrepresentation having a foreign DID of 3, the record with foreign recordID of 784 is associated with a foreign entity representation having aforeign DID of 4, and the remaining records are associated with aforeign entity representation having a foreign DID of 5, the modifiedTable III.2 may appear as, by way of non-limiting example:

TABLE VII.2 Foreign Record ID (Substituted with foreign DIDs) FN MN LNAGE CITY ST 1 Jon Ron Doe 1 John Ronald Doe Miami Florida 1 Jack RonaldDoe 1 Jon Doe 32 Florida 2 Jason Doe 31 Orlando 1 John Ronald Doe 5 JackMike Lee Orlando Florida 5 Jack Thomas Lee 39 Florida 4 Ron Paul Tampa 3David Joseph Smith Tampa Florida 3 David Jackson Smith Tampa Florida

The steps discussed in Section III may then be applied to the table asmodified. Thus, field match templates and partitions may be utilized asdescribed in Section III. Continuing the example that utilizes tableVII.2 in place of Table III.2, intermediate Table III.10 as modified mayappear, by way of non-limiting example, as follows.

TABLE VII.3 Foreign record ID (Substituted with FN MN LN AGE CITY STforeign DIDs) DID Weight Weight Weight Weight Weight Weight Score 1 1 56 4 0 0 0 15 1 1 5 0 4 0 0 0 9 1 2 0 0 4 0 0 0 4 1 4 0 6 4 0 0 0 10 1 40 0 4 0 0 0 4 1 4 0 0 4 0 0 0 4 1 2 5 6 4 0 0 3 18 1 6 5 0 4 0 0 0 9 1 65 0 4 0 0 3 12 1 6 5 0 4 0 0 3 12 1 6 5 0 4 0 0 3 12

This table may be treated as discussed in Section III to yield a tableto which the techniques of Section I may be applied to establish aconfidence level that the first ranked entry corresponds to the searchcriteria. Table VII.4 below illustrates how Table III.1 would appearupon continuing the example under discussion with the techniques of thissection applied to the example in Section III.

TABLE VII.4 Foreign Record ID (Substituted with FN MN LN AGE CITY STforeign DIDs) DID Weight Weight Weight Weight Weight Weight Score 1 1 56 4 0 0 0 15 1 2 0 0 4 0 0 0 4 1 4 0 6 4 0 0 0 10 1 2 5 6 4 0 0 3 18 1 65 0 4 0 0 3 12

Although Table VII.4 illustrates only a single foreign entityrepresentation, note that multiple foreign entity representations may bematched to multiple entity representations in the universal database.This table may be sorted according to score, and the techniques ofSection I may be applied to each group of records having the sameforeign record ID (substituted by foreign DIDs). Thus, each foreignentity representation will have an associated entity representation fromthe universal database and a known confidence level in the association.This information may be output in computer readable or human readableform.

Note that certain embodiments according to this section utilize thetechniques of Section VI as applied to the reflexive, symmetric andtransitive relation defined by the foreign entity representationidentification. That is, the relation “has the same foreign entityrepresentation identification” is reflexive, symmetric and transitive.Embodiments of the technique according to this section may be achievedby applying the techniques of Section V to such a relation andprocessing according to the techniques of Sections II or III to yield atable similar to Table VII.4. The techniques of Section I may be appliedto such a table to identify the entity representations from theuniversal database that correspond to the input search criteria.

VIII. Technique For Recycling Match Weight Calculations

Techniques according to this section may be used to recycle computationsperformed in a database linking operation for use in a search operation.For example, techniques according to this section may allow field valueweights computed as part of a database linking operation to be used in asearch operation. The field value weights may be computed according to,by way of non-limiting example, an iterative process as discussed indetail in the Second Generation Patents And Applications. The searchoperation may be any of the search operations discussed herein inSections I, II or III. Certain embodiments disclosed in those sectionsutilize field value weights in order to perform a search operation.According to techniques of the present section, these weights need notbe calculated from scratch. Instead, they may be efficiently derivedfrom calculations performed when the database was iteratively linked inorder to generate entity representations.

As discussed in detail in the Second Generation Patents AndApplications, a database may undergo an iterative process in order togenerate entity representations (i.e., records or linked collections ofrecords that refer to the same individual). Although the presenttechnique is not limited to the techniques of the Second GenerationPatents And Applications, it will be discussed in reference thereto forconvenience of discussion. Each iteration of a relevant iterativeprocess may include some or all of the following: calculating fieldvalue probabilities, calculating field value weights, calculating fieldprobabilities, calculating field weights, a linking process, atransitional linking process, a propagation operation, and a delinkingoperation. Each of these is discussed in detail in one or both of theFirst Generation Patents And Applications and the Second GenerationPatents And Applications. In certain embodiments, when the database isinitially populated with records, it undergoes multiple iterations ofthe iterative process in order to generate entity representations.Periodically (e.g., monthly), new records may be added to the database,and the database may be subjected to additional iterations.

As discussed in the Second Generation Patents And Applications, thefield value weights may be stored in an auxiliary copy of the database.More particularly, each field value weight may be appended to itsassociated field value as it appears in the record. Thus, for example,for a record that includes a first name of John, a last name of Doe, anda city of Fort Lauderdale, where the first name of John has a fieldvalue weight of 8, the last name of Doe has a field value weight of 12,and the city of Fort Lauderdale has a field value weight of 7, thesefield value weights may be stored, by way of non-limiting example, asdepicted in the following table.

TABLE VIII.1 FN LN CITY John 8 Doe 12 Fort Lauderdale 7

In some embodiments, these field value weights may be used as part of alinking operation to create or consolidate entity representations.Exemplary techniques for doing so are discussed in detail in the SecondGeneration Patents And Applications. In some embodiments, the originaldatabase omits the field value weights being appended to the fieldvalues. In some embodiments, records in the original database are linkedbased on the field value weights stored in the auxiliary database.

Thus, in some embodiments, each iteration in the linking process mayproceed by calculating field value weights and storing them in anauxiliary database and then performing a linking operation on theoriginal database based on such weights. At the beginning of eachiteration, the linkages reflected in the original database may bepropagated over to the auxiliary database. Note that in suchembodiments, the auxiliary database contains stale linkage informationat the start of each iteration. That is, at the start of each iteration,the auxiliary database may contain field value weights that were used inthe prior iteration to perform a linking operation in the originaldatabase, and after the linking operation, certain parameters used tocompute the field value weights may have changed. Accordingly, in someembodiments, the field value weights are re-computed at the start ofeach iteration, stored in an auxiliary database, and then used toperform a linking operation in the original database, thereby renderingthe field value weights stale in the sense that they no longeraccurately reflect the newly-linked original database.

In some embodiments, once the database has undergone one or more linkingoperations as discussed above, search operations may be performed on thedatabase. As discussed in Sections I-III herein, certain searchoperations may be performed using field value weights. Such searchoperation may be performed in a universal database and the searchresults utilized with respect to a foreign database. In someembodiments, the field value weights used in such search operations maybe calculated from scratch after the database has undergone an iterativelinking operation. In other embodiments, field value weights calculatedas part of the iterative linking process may be recycled for use insearch operations (e.g., search operations as discussed in SectionsI-III above). These latter embodiments are discussed in detailpresently.

According to techniques of this section, field value weight computedduring an iterative linking operation may be recycled for use in searchoperations. An exemplary embodiment of this technique is discussedpresently.

A technique for determining a sufficient (for the purpose of producingsufficiently accurate field value weights) number of iterations of aniterative linking process is presented. As discussed above and in theSecond Generation Patents And Applications, each iteration of aniterative linking operation is expected to produce more accurate fieldvalue weights, until a point is reached where the field value weightsstabilize such that further iterations do not result in further linkagesand field value weights do not change. In some embodiments, the fieldvalue weights are said to substantially stabilize if their values do notchange more than 10%. In other embodiments, such weights substantiallystabilize if their values do not change more than 5%. In still otherembodiments, if the field value weights do not change more than 1%, theyare said to have substantially stabilized.

Relatively accurate results may be obtained by using field value weightscalculated in an iteration prior to the iteration at which the weightsstabilize. By way of non-limiting example, in some embodiments, thenumber of iterations may be log(U) where U is the number of records orentity representations in the database. In such embodiments, iteratingthe linking process log(U) times may produce sufficiently accurate fieldvalue weights. As another non-limiting example, in some embodiments, thenumber of iterations may be log(M), where M is the average number ofrecords that correspond to the same individual. That is, M may be theaverage number of records that comprise an entity representation in afully linked database. Here, the term “average” may be, by way ofnon-limiting example, a mode, mean or median. Thus, in some embodiments,iterating the linking process log(M) times may produce sufficientlyaccurate field value weights.

Note that although field value weights as computed by an iteration priorto the point at which the weights stabilize may be used, the iterationmay continue until the stabilization point. That is, the linkingoperation iteration may continue after the field value weights areretrieved for use in a search operation.

Field value weights computed at any stage of a linking operation may berecycled for use in a search operation as follows. Once the field valueweights are entered into an auxiliary database and the original databaseundergoes a linking operation based upon such field value weights, theoriginal database may contain entity representations that differ fromthose in the auxiliary database. For example, the linking operation mayhave linked two entity representations that were previously unlinked. Inthe context of linkage using DIDs, prior to the linking, a first entityrepresentation may be linked via DID=123 and a second may be linked viaDID=456. After the linking, all records in the linked entityrepresentation may share the same DID of, for example, 123. Further, itmay be the case that no records with DID=456 exist once the linkingoperation occurs. Accordingly, it is possible that, after a giveniteration, the original database and the auxiliary database containdifferent entity representations. This may be accounted for by updatingthe auxiliary database, which contains the field value weights,according to the links present in the original database. This processmay be performed after any given iteration. The links of the auxiliarydatabase may be updated by comparing entity representations in eachdatabase, and updating the links in the auxiliary database to conform tothose present in the original database. Once the links in the auxiliarydatabase are updated, the auxiliary database contains all informationneeded to locate field value weights associated with a given entityrepresentation. These weights may then be used in search operationsperformed on either the original or the auxiliary database, for example,as discussed above in Sections I-III.

In some embodiments, no auxiliary database is utilized as part of alinking operation. In such embodiments, the field value weights computedat each iteration of the linking operation may be inserted into theoriginal database or stored in a separate table. Further, in suchembodiments, the weights computed at any stage of the iterative linkingoperation may be used in a search operation by retrieving such weightsdirectly.

FIG. 8 is a flowchart depicting an embodiment of an invention accordingto this section. At block 805, the relevant database is identified. Atblock 810, a logarithm of a parameter X is calculated. In someembodiments, the parameter X may be the total number of entityrepresentations (including unlinked records) in the database. In otherembodiments, the parameter X may be an average (e.g., mode, mean,median) number of records that comprise an entity representation in thedatabase. This latter term may be computed relative to the database asit stands at any given time, or may be predicted as what would bereflected by a fully linked database (e.g., a database for which allrecords for each individual are linked).

At block 815, field value weights are calculated and the databaseundergoes a linking operation as described in, e.g., the SecondGeneration Patents And Applications. These operations are iterated anumber of times. In some embodiments, the number of times is given bythe logarithm of the parameter X. Note that a logarithm of the parameterX may not be a whole number. In such instances, the logarithm of theparameter X may be rounded up, rounded down, or rounded to the nearestinteger. At block 820, once the linking operation is iterated, a searchis performed according to the techniques discussed in any section of thepresent disclosure. At block 825, the search results are rankedaccording to summed weight, and at block 830 an identifier for thehighest ranked result is output if a confidence level is sufficient.Block 835 reflects that iterating the linking operation may continuepast the logarithm of the parameter X number of times.

According to an exemplary embodiment, a method of recycling matchweights computed in a linking operation for use in a search operation,where the linking operation and the search operation are performed on afirst database, the database including a plurality of records, isdisclosed. The method includes computing, in an iterative process, aplurality of field value weights associated with field values present inat least some of the records, whereby the iterative process links atleast some records of the database. The method also includes performinga search operation on the database, where the search operation utilizesthe plurality of field value weights to identify at least one recordidentified by a search criteria, where the search criteria is derivedfrom information contained in a second database, where substantially allindividuals reflected in the second database are also reflected in thefirst database.

Various optional features of the above embodiment include the following.The plurality of field value weights may be computed prior to iterationN in the iterative process, where N is equal to a logarithm of a numberof records in the database. The plurality of field value weights may becomputed prior to iteration N in the iterative process, where N is equalto a logarithm of an average number of records in each entityrepresentation in the database.

IX. Conclusion

Any of the techniques disclosed herein may be applied to a portion of adatabase as opposed to the entirety of a database.

The techniques discussed herein may be combined with any of thetechniques disclosed in the First Generation Patents And Applications,the Second Generation Patents and Applications, and the '866Applications. The inventors explicitly consider such combinations at thetime of filing the present disclosure.

The equations, formulas and relations contained in this disclosure areillustrative and representative and are not meant to be limiting.Alternate equations may be used to represent the same phenomenadescribed by any given equation disclosed herein. In particular, theequations disclosed herein may be modified by adding error-correctionterms, higher-order terms, or otherwise accounting for inaccuracies,using different names for constants or variables, or using differentexpressions. Other modifications, substitutions, replacements, oralterations of the equations may be performed.

Certain embodiments of the inventions disclosed herein may output anyinformation contained in any record in a database.

Embodiments, or portions of embodiments, disclosed herein may be in theform of “processing machines,” such as general purpose computers, forexample. As used herein, the term “processing machine” is to beunderstood to include at least one processor that uses at least onememory. The at least one memory stores a set of instructions. Theinstructions may be either permanently or temporarily stored in thememory or memories of the processing machine. The processor executes theinstructions that are stored in the memory or memories in order toprocess data. The set of instructions may include various instructionsthat perform a particular task or tasks, such as those tasks describedherein. Such a set of instructions for performing a particular task maybe characterized as a program, software program, or simply software.

As noted above, the processing machine executes the instructions thatare stored in the memory or memories to process data. This processing ofdata may be in response to commands by a user or users of the processingmachine, in response to previous processing, in response to a request byanother processing machine and/or any other input, for example.

As noted above, the processing machine used to implement embodiments maybe a general purpose computer. However, the processing machine describedabove may also utilize any of a wide variety of other technologiesincluding a special purpose computer, a computer system including amicrocomputer, mini-computer or mainframe for example, a programmedmicroprocessor, a micro-controller, a peripheral integrated circuitelement, a CSIC (Customer Specific Integrated Circuit) or ASIC(Application Specific Integrated Circuit) or other integrated circuit, alogic circuit, a digital signal processor, a programmable logic devicesuch as a FPGA, PLD, PLA or PAL, or any other device or arrangement ofdevices that is capable of implementing the steps of the processes ofthe invention. In particular, the hardware described in the FirstGeneration Patents And Applications may be used for any embodimentdisclosed herein. A cluster of personal computers or blades connectedvia a backplane (network switch) may be used to implement someembodiments.

The processing machine used to implement the invention may utilize asuitable operating system. Thus, embodiments of the invention mayinclude a processing machine running the Microsoft Windows™ Vista™operating system, the Microsoft Windows™ XP™ operating system, theMicrosoft Windows™ NT™ operating system, the Windows™ 2000 operatingsystem, the Unix operating system, the Linux operating system, the Xenixoperating system, the IBM AIX™ operating system, the Hewlett-Packard UX™operating system, the Novell Netware™ operating system, the SunMicrosystems Solaris™ operating system, the OS/2™ operating system, theBeOS™ operating system, the Macintosh operating system, the Apacheoperating system, an OpenStep™ operating system or another operatingsystem or platform.

It is appreciated that in order to practice the method of the inventionas described above, it is not necessary that the processors and/or thememories of the processing machine be physically located in the samegeographical place. That is, each of the processors and the memoriesused by the processing machine may be located in geographically distinctlocations and connected so as to communicate in any suitable manner.Additionally, it is appreciated that each of the processor and/or thememory may be composed of different physical pieces of equipment.Accordingly, it is not necessary that the processor be one single pieceof equipment in one location and that the memory be another single pieceof equipment in another location. That is, it is contemplated, forexample, that the processor may be two ore more pieces of equipment intwo different physical locations. The two ore more distinct pieces ofequipment may be connected in any suitable manner. Additionally, thememory may include two or more portions of memory in two or morephysical locations.

To explain further, processing as described above is performed byvarious components and various memories. However, it is appreciated thatthe processing performed by two or more distinct components as describedabove may, in accordance with a further embodiment of the invention, beperformed by a single component. Further, the processing performed byone distinct component as described above may be performed by two ormore distinct components. In a similar manner, the memory storageperformed by two or more distinct memory portions as described abovemay, in accordance with a further embodiment of the invention, beperformed by a single memory portion. Further, the memory storageperformed by one distinct memory portion as described above may beperformed by two or more memory portions.

Further, various technologies may be used to provide communicationbetween the various processors and/or memories, as well as to allow theprocessors and/or the memories of the invention to communicate with anyother entity; e.g., so as to obtain further instructions or to accessand use remote memory stores, for example. Such technologies used toprovide such communication might include a network, the Internet,Intranet, Extranet, LAN, an Ethernet, or any client server system thatprovides communication, for example. Such communications technologiesmay use any suitable protocol such as TCP/IP, UDP, or OSI, for example.

As described above, a set of instructions is used in the processing ofembodiments. The set of instructions may be in the form of a program orsoftware. The software may be in the form of system software orapplication software, for example. The software might also be in theform of a collection of separate programs, a program module within alarger program, or a portion of a program module, for example. Thesoftware used might also include modular programming in the form ofobject oriented programming. The software tells the processing machinewhat to do with the data being processed.

Further, it is appreciated that the instructions or set of instructionsused in the implementation and operation of the invention may be in asuitable form such that the processing machine may read theinstructions. For example, the instructions that form a program may bein the form of a suitable programming language, which is converted tomachine language or object code to allow the processor or processors toread the instructions. That is, written lines of programming code orsource code, in a particular programming language, are converted tomachine language using a compiler, assembler or interpreter. The machinelanguage is binary coded machine instructions that are specific to aparticular type of processing machine, e.g., to a particular type ofcomputer. The computer understands the machine language.

Any suitable programming language may be used in accordance with thevarious embodiments of the invention. Illustratively, the programminglanguage used may include Enterprise Control Language (“ECL,” availablefrom LexisNexis), assembly language, Ada, APL, C, C++, dBase, Fortran,Java, Modula-2, Pascal, REXX, Visual Basic, and/or JavaScript, forexample. Further, it is not necessary that a single type of instructionsor single programming language be utilized in conjunction with theoperation of the system and method of the invention. Rather, any numberof different programming languages may be utilized as is necessary ordesirable.

Also, the instructions and/or data used in the practice of the inventionmay utilize any compression or encryption technique or algorithm, as maybe desired. An encryption module might be used to encrypt data. Further,files or other data may be decrypted using a suitable decryption module,for example.

It is to be appreciated that the set of instructions, e.g., thesoftware, that enables the computer operating system to perform theoperations described above may be contained on any of a wide variety ofmedia or medium, as desired. Further, the data that is processed by theset of instructions might also be contained on any of a wide variety ofmedia or medium. That is, the particular medium, i.e., the memory in theprocessing machine, utilized to hold the set of instructions and/or thedata used in the invention may take on any of a variety of physicalforms or transmissions, for example. Illustratively, the medium may bein the form of paper, paper transparencies, a compact disk, a DVD, anintegrated circuit, a hard disk, a floppy disk, an optical disk, amagnetic tape, a RAM, a ROM, a PROM, a EPROM, a wire, a cable, a fiber,communications channel, a satellite transmissions or other remotetransmission, as well as any other medium or source of data that may beread by the processors of the invention.

Further, the memory or memories used in the processing machine thatimplements an embodiment may be in any of a wide variety of forms toallow the memory to hold instructions, data, or other information, as isdesired. Thus, the memory might be in the form of a database to holddata. The database might use any desired arrangement of files such as aflat file arrangement or a relational database arrangement, for example.

In some embodiments, a variety of “user interfaces” may be utilized toallow a user to interface with the processing machine or machines thatare used to implement the embodiment. As used herein, a user interfaceincludes any hardware, software, or combination of hardware and softwareused by the processing machine that allows a user to interact with theprocessing machine. A user interface may be in the form of a dialoguescreen for example. A user interface may also include any of a mouse,touch screen, keyboard, voice reader, voice recognizer, dialogue screen,menu box, list, checkbox, toggle switch, a pushbutton or any otherdevice that allows a user to receive information regarding the operationof the processing machine as it processes a set of instructions and/orprovide the processing machine with information. Accordingly, the userinterface is any device that provides communication between a user and aprocessing machine. The information provided by the user to theprocessing machine through the user interface may be in the form of acommand, a selection of data, or some other input, for example.

As discussed above, a user interface is utilized by the processingmachine that performs a set of instructions such that the processingmachine processes data for a user. The user interface is typically usedby the processing machine for interacting with a user either to conveyinformation or receive information from the user. However, it should beappreciated that in accordance with some embodiments of the system andmethod of the invention, it is not necessary that a human user actuallyinteract with a user interface used by the processing machine of theinvention. Rather, it is also contemplated that the user interface ofthe invention might interact, e.g., convey and receive information, withanother processing machine, rather than a human user. Accordingly, theother processing machine might be characterized as a user. Further, itis contemplated that a user interface utilized in the system and methodof the invention may interact partially with another processing machineor processing machines, while also interacting partially with a humanuser.

It will be readily understood by those persons skilled in the art thatembodiments of the present inventions are susceptible to broad utilityand application. Many embodiments and adaptations of the presentinventions other than those herein described, as well as manyvariations, modifications and equivalent arrangements, will be apparentfrom or reasonably suggested by the present invention and foregoingdescription thereof, without departing from the substance or scope ofthe invention.

Accordingly, it is to be understood that this disclosure is onlyillustrative and exemplary and is made to provide an enablingdisclosure. Accordingly, the foregoing disclosure is not intended to beconstrued or to limit the present invention or otherwise to exclude anyother such embodiments, adaptations, variations, modifications orequivalent arrangements.

1. A method of identifying an entity representation in an electronicuniversal database that corresponds to an entity representation in anelectronic foreign database, each database comprising a plurality ofentity representations, each entity representation comprising aplurality of linked records, each record comprising a plurality offields, each field capable of containing a field value, each field valueassociated with a field value weight, the method comprising: receiving aplurality of search criteria field values; determining an entityrepresentation in the foreign database corresponding to the searchcriteria field values; forming a comprehensive search criteriacomprising a plurality of field values from a plurality of records fromthe entity representation in the foreign database corresponding to thesearch criteria field values, wherein the comprehensive search criteriacomprises at least two non-identical field values associated with a samefield; determining a highest ranked entity representation in theuniversal database according to summed field value weights for fieldvalues matching the comprehensive search criteria; calculating aconfidence level reflecting a likelihood that the highest ranked entityrepresentation corresponds to the plurality of search criteria fieldvalues; and outputting, if the confidence level exceeds a predeterminedthreshold, an identifier for the highest ranked entity representation.2. The method of claim 1, wherein the comprehensive search criteriacomprises a plurality of pairs of non-identical field values, each pairof the plurality of pairs of field values for a same field.
 3. Themethod of claim 2, wherein the comprehensive search criteria comprisesat least three non-identical field values for a same field, wherein theat least three non-identical field values comprise one of the pluralityof pairs of non-identical field values.
 4. The method of claim 1,wherein the plurality of search criteria field values are included in asearch query.
 5. The method of claim 1, wherein the plurality of searchcriteria field values are included in a batch file.
 6. The method ofclaim 1, wherein each field value weight comprises a probability that anarbitrary entity representation in the universal database comprises acorresponding field value in a field of a record in the arbitrary entityrepresentation.
 7. The method of claim 1, wherein the identifiercomprises a social security number.
 8. A method of identifying aplurality of entity representations in an electronic universal databasethat correspond to a plurality of entity representations in anelectronic foreign database, each database comprising a plurality ofentity representations, each entity representation comprising aplurality of linked records, each record comprising a plurality offields, each field capable of containing a field value, each field valueassociated with a field value weight, the method comprising: receiving aplurality of search criteria field values corresponding to a pluralityof entity representations in the foreign database; determining aplurality of entity representations in the foreign databasecorresponding to the plurality of search criteria field values; forminga plurality of comprehensive search criteria, one comprehensive searchcriteria for each of the plurality of entity representations in theforeign database corresponding to the search criteria field values, eachcomprehensive search criteria comprising a plurality of field valuesfrom a plurality of records from the corresponding entity representationin the foreign database, wherein each comprehensive search criteriacomprises at least two non-identical field values associated with a samefield; for each comprehensive search criteria, determining acorresponding highest ranked entity representation in the universaldatabase according to summed field value weights for field valuesmatching the comprehensive search criteria; for each highest rankedentity representation, calculating an associated confidence levelreflecting a likelihood that the highest ranked entity representation iscorrect for the corresponding comprehensive search criteria; and foreach highest ranked entity representation, outputting, if the associatedconfidence level exceeds a predetermined threshold, an identifier forthe highest ranked entity representation.
 9. The method of claim 8,further comprising joining separate search identifiers to portions ofthe plurality of search criteria field values.
 10. The method of claim8, wherein each comprehensive search criteria comprises a plurality ofpairs of non-identical field values, each pair of the plurality of pairsof field values for a same field.
 11. The method of claim 10, wherein atleast one of the comprehensive search criteria comprises at least threenon-identical field values for a same field, wherein the at least threenon-identical field values comprise one of the plurality of pairs ofnon-identical field values.
 12. The method of claim 8, wherein theplurality of search criteria field values are included in a searchquery.
 13. The method of claim 8, wherein the plurality of searchcriteria field values are included in a batch file.
 14. The method ofclaim 8, wherein each field value weight comprises a probability that anarbitrary entity representation in the universal database comprises acorresponding field value in a field of a record in the arbitrary entityrepresentation.
 15. The method of claim 8, wherein the identifiercomprises a social security number.
 16. A system for identifying anentity representation in an electronic universal database thatcorresponds to an entity representation in an electronic foreigndatabase, each database comprising a plurality of entityrepresentations, each entity representation comprising a plurality oflinked records, each record comprising a plurality of fields, each fieldcapable of containing a field value, each field value associated with afield value weight, the system comprising: an electronic universaldatabase comprising a plurality of entity representations, each entityrepresentation comprising a plurality of linked records, each recordcomprising a plurality of fields, each field capable of containing afield value, each field value associated with a field value weight; anelectronic memory storing a plurality of search criteria field values; aprocessor programmed to determine an entity representation in theforeign database corresponding to the search criteria field values; aprocessor programmed to form and store a comprehensive search criteriacomprising a plurality of field values from a plurality of records fromthe entity representation in the foreign database corresponding to thesearch criteria field values, wherein the comprehensive search criteriacomprises at least two non-identical field values associated with a samefield; a processor programmed to determine a highest ranked entityrepresentation in the universal database according to summed field valueweights for field values matching the comprehensive search criteria; aprocessor programmed to calculate a confidence level reflecting alikelihood that the highest ranked entity representation corresponds tothe plurality of search criteria field values; and an output configuredto output, if the confidence level exceeds a predetermined threshold, anidentifier for the highest ranked entity representation.
 17. The systemof claim 16, wherein the comprehensive search criteria comprises aplurality of pairs of non-identical field values, each pair of theplurality of pairs of field values for a same field.
 18. The method ofclaim 17, wherein the comprehensive search criteria comprises at leastthree non-identical field values for a same field, wherein the at leastthree non-identical field values comprise one of the plurality of pairsof non-identical field values.
 19. The system of claim 16, wherein theplurality of search criteria field values are included in a searchquery.
 20. The system of claim 16, wherein the plurality of searchcriteria field values are included in a batch file.
 21. The system ofclaim 16, wherein each field value weight comprises a probability thatan arbitrary entity representation in the universal database comprises acorresponding field value in a field of a record in the arbitrary entityrepresentation.
 22. The system of claim 16, wherein the output comprisesa human readable display.
 23. The system of claim 16, wherein theidentifier comprises a social security number.
 24. A system ofidentifying a plurality of entity representations in an electronicuniversal database that correspond to a plurality of entityrepresentations in an electronic foreign database, each databasecomprising a plurality of entity representations, each entityrepresentation comprising a plurality of linked records, each recordcomprising a plurality of fields, each field capable of containing afield value, each field value associated with a field value weight, thesystem comprising: an electronic universal database comprising aplurality of entity representations, each entity representationcomprising a plurality of linked records, each record comprising aplurality of fields, each field capable of containing a field value,each field value associated with a field value weight; an electronicmemory storing a plurality of search criteria field values correspondingto a plurality of entity representations in the foreign database; aprocessor programmed to determine a plurality of entity representationsin the foreign database corresponding to the plurality of searchcriteria field values; a processor programmed to form and store aplurality of comprehensive search criteria, one comprehensive searchcriteria for each of the plurality of entity representations in theforeign database corresponding to the search criteria field values, eachcomprehensive search criteria comprising a plurality of field valuesfrom a plurality of records from the corresponding entity representationin the foreign database, wherein each comprehensive search criteriacomprises at least two non-identical field values associated with a samefield; a processor programmed to, for each comprehensive searchcriteria, determine a corresponding highest ranked entity representationin the universal database according to summed field value weights forfield values matching the comprehensive search criteria; a processorprogrammed to, for each highest ranked entity representation, calculatean associated confidence level reflecting a likelihood that the highestranked entity representation is correct for the correspondingcomprehensive search criteria; and an output configured to, for eachhighest ranked entity representation, output, if the associatedconfidence level exceeds a predetermined threshold, an identifier forthe highest ranked entity representation.
 25. The system of claim 24,further comprising a processor programmed to join separate searchidentifiers to portions of the plurality of search criteria fieldvalues.
 26. The system of claim 24, wherein each comprehensive searchcomprises a plurality of pairs of non-identical field values, each pairof the plurality of pairs of field values for a same field.
 27. Thesystem of claim 26, wherein at least one of the comprehensive searchcriteria comprises at least three non-identical field values for a samefield, wherein the at least three non-identical field values compriseone of the plurality of pairs of non-identical field values.
 28. Thesystem of claim 24, wherein the plurality of search criteria fieldvalues are included in a search query.
 29. The system of claim 24,wherein the plurality of search criteria field values are included in abatch file.
 30. The system of claim 24, wherein each field value weightcomprises a probability that an arbitrary entity representation in theuniversal database comprises a corresponding field value in a field of arecord in the arbitrary entity representation.
 31. The system of claim24, wherein the output comprises a human readable display.
 32. Thesystem of claim 24, wherein the identifier comprises a social securitynumber.